A strategy for materials price risk mitigation

A STRATEGY FOR MATERIALS PRICE RISK MITIGATION

by REBECCA NORWOOD MACDONALD W. EDWARD BACK, COMMITTEE CHAIR DAVID GRAU K. CLARK MIDKIFF GARY P. MOYNIHAN JAMES A. RICHARDSON

A DISSERTATION

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Civil, Construction, and Environmental Engineering in the Graduate School of The University of Alabama

TUSCALOOSA, ALABAMA

2013

Copyright Rebecca Norwood Macdonald 2013 ALL RIGHTS RESERVED

ABSTRACT Construction projects are too commonly over budget and regularly contend with extreme fluctuations with regard to strategic materials pricing. The engineering and construction (E&C) industry continues to struggle with effective cost control techniques and is in need of new tools and/or strategies to successfully address these mounting issues. A research study was undertaken for construction projects, inclusive of any delivery type, scale, or location, to identify existing strategies currently in use by the E&C industry as well as other industries. A historical markup or price escalator for contingencies is not a universal solution for current and future market volatility. Their use was found to be inadequate 40% of the time. An opportunity exists for the E&C industry to adopt established strategies currently in use by other industries to improve project performance. A model for materials price risk mitigation for particular use in the E&C industry was developed and proposed. Using real-world projects the model is exercised to verify the positive, mitigating impact of derivative usage. The findings are that financial tools used by other industries are not only applicable in the construction industry but unique in project specific application. Hedging, in particular, was found to be effective more than 70% of the time. More specifically, hedging diminishes steel pricing escalation by 21% compared to non-hedging. With the steel market still in its infancy, the growth potential is vast. Other financial instruments and commodity markets should also be analyzed for applicable benefit to the construction industry. Best practices for installing organizational structure and processes for executing the model could ii

be determined. Possible limitations are the lack of transparency in certain market structures and implementation costs. Ultimately, a better informed and more financially savvy E&C industry will emerge.

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DEDICATION This dissertation is dedicated to everyone in academia who helped me and guided me through the trials and tribulations of creating this manuscript. I am also grateful for my prior industry experience and education that provided the inspiration for this research subject area. I would also be remiss not to thank my family and friends for their unwavering support. I will continue to be a life-long learner and have found the following quote to truly resonate with me. Education is a progressive discovery of our own ignorance. –Will Durant

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LIST OF ABBREVIATIONS AND SYMBOLS Abbreviations: 1M

1 month

3M

3 months

6M

6 months

12M

12 months

18M

18 months

2Y

2 years

3Y

3 years

5Y

5 years

APC

Average Percent Change

CII

Construction Industry Institute

CME

CME Group

DGCX

Dubai Gold and Commodities Exchange

E&C

Engineering and Construction

EEI

Edison Electric Institute

EIA

Energy Information Administration

H

Hedge

HRC

U.S. Midwest Domestic Hot-Rolled Coil Steel Index Futures

kV

Kilo Volt v

LME

London Metal Exchange Steel Billet

LMEX

London Metal Exchange

MISO

Midwest Independent Transmission System Operator

OTC

Over-the-Counter

PJM

PJM Interconnection

PPI

Producer Price Index

S

Standard

WTI

West Texas Intermediate Crude Oil

Symbols: =

Equal to

<

Less than

EP

Effective Price

F

Futures price at time of initiation or hedge is set up

F

Futures price at time of collusion or material is purchased

S

Asset price at time of initiation

S

Asset price at time of conclusion or purchase of material

0

t

0

t

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ACKNOWLEDGMENTS Many people have contributed to the production of this dissertation. I owe my gratitude to all those whom have made this dissertation possible and acknowledge them for their support, encouragement, and understanding. I am forever indebted to Dr. W. Edward Back, my advisor, for his encouragement, guidance, patience and support in this endeavor. I am deeply grateful to the other members of my committee, Dr. David Grau, Dr. K. Clark Midkiff, Dr. Gary P. Moynihan, and Dr. James Richardson for their comments and commitment. I would also like to thank other faculty and staff at The University of Alabama for making my graduate experience one that I will cherish forever.

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CONTENTS ABSTRACT .................................................................................................................................... ii DEDICATION ............................................................................................................................... iv LIST OF ABBREVIATIONS AND SYMBOLS ............................................................................v ACKNOWLEDGMENTS ............................................................................................................ vii LIST OF TABLES ........................................................................................................................ xii LIST OF FIGURES ..................................................................................................................... xiv CHAPTER 1: Introduction ..............................................................................................................1 1.1 Background ....................................................................................................................1 1.1.1 Materials ...............................................................................................................3 1.1.2 Price Volatility ......................................................................................................4 1.1.3 Risk ......................................................................................................................8 1.1.4 Traditional Mitigation Techniques .......................................................................9 1.1.5 New Mitigation Techniques ................................................................................11 1.2 Problem Statement ..........................................................................................................12 1.3 Research Scope and Objectives ......................................................................................12 1.4 Research Methodology ...................................................................................................13 1.5 Outline of Dissertation ....................................................................................................14 CHAPTER 2: Literature Review ...................................................................................................16 2.1 Introduction .....................................................................................................................16 2.2 Uncertainties ...................................................................................................................16 2.3 Risk .................................................................................................................................17 2.5 Traditional Construction Mitigation ...............................................................................20 2.4 Other Industries Mitigation .............................................................................................21 2.5 Summary .........................................................................................................................22 viii

CHAPTER 3: Financial Risk Mitigation Technique .....................................................................24 3.1 Description ......................................................................................................................24 3.2 Explanation .....................................................................................................................24 3.2.1 Swap ....................................................................................................................26 3.2.2 Forward ...............................................................................................................26 3.2.3 Futures.................................................................................................................27 3.2.4 Option .................................................................................................................27 3.2.5 General ................................................................................................................29 3.3 Case Study Application...................................................................................................29 3.4 Summary .........................................................................................................................35 CHAPTER 4: Statistical Analysis of Applied Hedging Strategies ................................................36 4.1 Introduction .....................................................................................................................36 4.2 Data Collection ...............................................................................................................37 4.3 Simulation .......................................................................................................................40 4.4 Findings...........................................................................................................................44 4.4.1 Short Duration .....................................................................................................44 4.4.1.1 One Month ..............................................................................................45 4.4.1.2 Three Months ..........................................................................................47 4.4.1.3 Six Months ..............................................................................................49 4.4.1.4 Summary .................................................................................................51 4.4.2 Medium Duration ................................................................................................52 4.4.2.1 Twelve Months .......................................................................................53 4.4.2.2 Eighteen Months .....................................................................................55 4.4.2.4 Summary .................................................................................................57 4.4.3 Long Duration .....................................................................................................57 4.4.3.1 Two Years ..............................................................................................58 4.4.3.2 Three Years ............................................................................................60 4.4.3.3 Five Years ...............................................................................................62 4.4.3.4 Summary .................................................................................................63 4.5 Statistical Significance ....................................................................................................64 4.6 Summary ........................................................................................................................70 ix

CHAPTER 5: Proposed Risk Mitigation Strategy for Construction Projects................................72 5.1 Introduction .....................................................................................................................72 5.2 Model ..............................................................................................................................74 5.2.1 Guidance .............................................................................................................75 5.2.2 Identification .......................................................................................................75 5.2.3 Assessment ..........................................................................................................76 5.2.4 Determination .....................................................................................................78 5.2.5 Implementation ...................................................................................................79 5.2.6 Settlement ...........................................................................................................80 5.2.7 Evaluation ...........................................................................................................80 CHAPTER 6: Industry Application and Validation ......................................................................81 6.1 Introduction .....................................................................................................................81 6.2 Industry Description........................................................................................................82 6.3 Guidelines .......................................................................................................................83 6.4 Identification ...................................................................................................................83 6.5 Assessment ......................................................................................................................84 6.6 Determination .................................................................................................................85 6.7 Implementation ...............................................................................................................85 6.8 Settlement .......................................................................................................................86 6.9 Evaluation .......................................................................................................................87 6.10 Summary .......................................................................................................................88 CHAPTER 7: Conclusions/Recommendations..............................................................................90 7.1 Conclusion/ Recommendations ......................................................................................90 7.2 Limitations ......................................................................................................................91 7.3 Future Work ....................................................................................................................92 REFERENCES ..............................................................................................................................93 APPENDIX A: WTI Price Data ....................................................................................................98 APPENDIX B: Summary of Simulated Actions .........................................................................101 APPENDIX C: Statistical Tests ...................................................................................................124 C.1 Equal Variance Tests ....................................................................................................124 x

C.2 Mean Tests ...................................................................................................................131 C.3 Median Tests ................................................................................................................139 APPENDIX D: Transmission Line Projects ................................................................................145

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LIST OF TABLES Table 1.1: Time Period and Total Price Escalation of Steel, Crude, and Cement ...........................6 Table 1.2: Calculated Data of Steel, Crude, and Cement ..............................................................10 Table 2.1: Summary of Construction Risk Management Tools and Techniques ..........................18 Table 3.1: Monthly Swap Settlements for Scenario 1 ...................................................................32 Table 3.2: Summary of Scenarios and Project Cost Impact. .........................................................35 Table 4.1: Time Periods and Number of Data Points ....................................................................38 Table 4.2: Snapshot of First Three Comparison Dates and Last Date for Each Coverage Length Investigated ...................................................................................................41 Table 4.3: Analysis Results for Short Duration Actions................................................................45 Table 4.4: Analysis Results for Medium Duration Actions ...........................................................52 Table 4.5: Analysis Results for Long Duration Actions ................................................................58 Table 4.6: Two-Sample T-Test and CI: WTI 1 M H, WTI 1 M S .................................................65 Table 4.7: Test for Equal Variances: WTI 1 M H, WTI 1 M S .....................................................65 Table 4.8: Parametric Test Results ................................................................................................66 Table 4.9: Mann-Whitney Test and CI: WTI 1 M H, WTI 1 M S .................................................68 Table 4.10: Test for Equal Variances: WTI 1 M H, WTI 1 M S ...................................................68 Table 4.11: Non-Parametric Test results .......................................................................................68 Table 5.1: Typical Commodity Markets for Construction Projects ...............................................76 Table 5.2: Example of Commodity Contract Terms ......................................................................77 Table 6.1: Representative Transmission Line Projects ..................................................................83 Table 6.2: Randomly Selected Projects .........................................................................................86 xii

Table 6.3: Results of Settlement ....................................................................................................87 Table 6.4: Comparison of Results ..................................................................................................88

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LIST OF FIGURES Figure 1.1: Typical construction project exchanges ........................................................................2 Figure 1.2: Conceptual cost structure of construction materials with commodity volatility ...........4 Figure 1.3: Producer price index of steel, crude, and cement ..........................................................5 Figure 1.4: Steel price, percent change, and shocks by year ...........................................................7 Figure 1.5: Crude price, percent change, and shocks by year ..........................................................7 Figure 1.6: Cement price, percent change, and shocks by year .......................................................8 Figure 1.7: Transfer of risk through derivative market..................................................................11 Figure 1.8: Summary of research methodology .............................................................................14 Figure 3.1: Depiction of four common hedge strategies applicable for the E&C industry ...........25 Figure 3.2: A common hedge strategy, swap .................................................................................26 Figure 3.3: A common hedge strategy, forward ............................................................................27 Figure 3.4: A common hedge strategy, call option ........................................................................28 Figure 3.5: An advanced hedge strategy, premium collar .............................................................29 Figure 3.6: Steel prices for the entire duration of the case study Source: U.S. Bureau of Labor ..30 Figure 3.7: Depiction of four hedge strategies for the case study of steel .....................................34 Figure 4.1: WTI monthly spot and futures pricing ........................................................................38 Figure 4.2: LME monthly spot and futures pricing .......................................................................39 Figure 4.3: HRC monthly spot and futures pricing........................................................................39 Figure 4.4: Summary of the simulated actions for one month WTI using hedge ..........................43 Figure 4.5: Summary of the simulated actions for one month WTI using standard ......................43 Figure 4.6: One month hedge and standard actions for WTI .........................................................46 Figure 4.7: One month hedge and standard actions for LME ........................................................46 xiv

Figure 4.8: One month hedge and standard actions for HRC ........................................................47 Figure 4.9: Three months hedge and standard actions for WTI ....................................................48 Figure 4.10: Three months hedge and standard actions for LME..................................................48 Figure 4.11: Three month hedge and standard actions for HRC ...................................................49 Figure 4.12: Six months hedge and standard actions for WTI ......................................................50 Figure 4.13: Six months hedge and standard actions for LME......................................................50 Figure 4.14: Six months hedge and standard actions for HRC ......................................................51 Figure 4.15: Comparison of two actions for WTI, LME and HRC on short durations ...............52 Figure 4.16: Twelve months hedge and standard actions for WTI ................................................53 Figure 4.17: Twelve months hedge and standard actions for LME ...............................................54 Figure 4.18: Twelve months hedge and standard actions for HRC ...............................................54 Figure 4.19: Eighteen months hedge and standard actions for WTI..............................................55 Figure 4.20: Eighteen months hedge and standard actions for LME .............................................56 Figure 4.21: Eighteen months hedge and standard actions for HRC .............................................56 Figure 4.22: Comparison of two actions for WTI, LME and HRC on medium duration ............57 Figure 4.23: Two years hedge and standard actions for WTI ........................................................59 Figure 4.24: Two years hedge and standard actions for LME .......................................................59 Figure 4.25: Two years hedge and standard actions for HRC .......................................................60 Figure 4.26: Three years hedge and standard actions for WTI ......................................................61 Figure 4.27: Three years hedge and standard actions for LME .....................................................61 Figure 4.28: Three years hedge and standard actions for HRC .....................................................62 Figure 4.29: Five years hedge and standard actions for WTI ........................................................63 Figure 4.30: Comparison of hedge and standard actions for WTI, LME and HRC on long duration .....................................................................................................................64 Figure 4.31: Project risk influence diagram ...................................................................................71

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Figure 5.1: Model with key action items for materials price mitigation .......................................74 Figure 5.2: Hedge decision tree .....................................................................................................79

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CHAPTER 1: Introduction

1.1 BACKGROUND Current major construction projects whether civil (highways, tunnels, sewers) or industrial (power plants) can and do involve multiple years of planning, design, engineering, and construction regardless of location in US or the world. These 21st Century projects cost billions of dollars in manpower and materials. Volatile and fluctuating resource prices over time could create tensions, conflicts, and/or friction on a generally static project. Such major projects readily expose these concerns but the same risks are present on any significant project and deserve mitigation and enhanced management. For the engineering construction (E&C) industry, a particularly important success criterion is to meet financial expectations. However, in today’s global construction engineering environment, projects are too often over budget and fail to meet financial expectations. These expectations are based on projections and fraught with cost uncertainties that could be further exacerbated by the typical project methodology used by participants. In such methodology, budgets are set early in the project life cycle or upon bid award. Then procurement of materials is placed when they are needed setting the ultimate price. This delay can create discrepancies between the initial budgeted cost and final purchase price as a result of market dynamics, causing a project to not meet its financial expectations. The typical participants in a construction project are the owner, contractors, suppliers, and the markets. Interactions between each participant are depicted in Figure 1.1. In simple 1

terms, the owner contractually allocates risks to the contractor, who ultimately delivers the completed project. The contractor procures the necessary materials from approved suppliers. The suppliers depend on the markets for the underlying resources, the raw goods or commodities. These exchanges typically occur in a tiered nature, allowing for each participant to increase any rising costs through mark-ups, which may inflate these costs of materials and the project. Therefore, in these traditional procurement arrangements the owner and contractor are usually removed from influencing or controlling the final costs.

Figure 1.1: Typical construction project exchanges These exchanges take place in any sector of the construction industry or scale of a project. However the quantity of material or magnitude of costs varies greatly, generating 2

greater impact for large scale, industrial or infrastructure type projects. Exchanges occur similarly regardless of site location, domestic or international. Furthermore the project delivery and contract strategy is not a determining factor, yet it often determines the bearer of the risk.

1.1.1 Materials If combined with related services like equipment, construction materials encompass a large percentage, 50-60%, of the total cost of a project (CII, 2013). When left unmanaged, materials may have greater significant impact on project cost. Materials price uncertainties are pervasive throughout the project lifecycle, occurring at project initiation and continuing through execution. Prices may fluctuate for a variety of factors, including exchange rate fluctuations, supply stream/transportation issues, commodity market structure, public information, inflationary conditions, technology, innovations, regulatory law, world events, politics, environment, and customer preferences. The E&C industry consumes a vast range of materials, the costs of which are influenced by the behavior of several key factors. Each construction material has the following conceptual factor cost structure as depicted in Figure 1.2. Even when overheads and profit margins are controlled by the project participant, the underlying resource and production cost of the material can change without control. However each factor is independent from the other and can fluctuate or vary tremendously. Production costs could change overtime through innovation and technique, yet underlying resource prices are predominantly market driven.

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Total Sales Price

Underlying Resource (commodity) Production Costs Overheads Profit Margins

High Price

Low Price

Figure 1.2: Conceptual cost structure of construction materials with commodity volatility The materials required on construction projects can be categorized as bulk, fabricated, or engineered. Each has at least one underlying resource and is processed to some degree before implementable on a project site. With materials expenditures equaling upwards of 40% of total costs on infrastructure projects, this can have a major impact.

1.1.2 Price Volatility Fluctuations of materials’ prices, which are volatile, are a driver of project costs. Volatility is a measure of the amount and speed of price changes, regardless of whether it is a financial increase or decrease. This is important not only because price escalations may occur, but because price corrections also potentially impact allocation of resources and project selection. The owner may not proceed or a contractor may choose to inflate bids because of increased prices.

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Within the heavy construction sector particularly, a substantial quantity of construction materials are produced with the underlying resource (“commodity”) of steel, cement, and crude. Examples of these commodities uses on projects are in structural components for steel, concrete for cement and asphalt for crude. History has shown varying degrees of volatility in these commodities. Figure 1.3 depicts the historical movements of these major commodities calculated by the U.S. Bureau of Labor statistics (2012) and published as the Producer Price Index (PPI).

$300 $250 $200

STEEL CRUDE CEMENT

$150 $100 $50

1926 1929 1932 1935 1938 1941 1944 1947 1950 1953 1956 1959 1962 1965 1968 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010

$0

Figure 1.3: Producer price index of steel, crude, and cement

As illustrated in Figure 1.3, the PPI of steel, crude, and cement is increasing over time. The total escalation of each commodity over the available time period is listed in Table 1.1.

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Table 1.1: Time Period and Total Price Escalation of Steel, Crude, and Cement PPI

Time Period

Steel Crude Cement

1926-2011 1947-2011 1971-2011

Total Price Escalation 2142.66% 3121.73% 410.98%

These values are substantial but not uniform from year to year. These price escalations can be better reflected in the calculation of year to year percent change. Further, the calculated average of the yearly percent change will represent a baseline to reflect the fluctuations of values over time. A “shock” is typically referred to as a moment in time when a commodity price increases suddenly. For this analysis, a shock is when the value is above the average. The average yearly prices, yearly percent change, and the average yearly percent change of each commodity are depicted for steel in Figure 1.4, crude in Figure 1.5, and cement in Figure 1.6 (U.S. Bureau of Labor, 2012).

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$300

Average Yearly Price

40.00%

Yearly Percent Change

$250

30.00% Average Yearly Percent Change

20.00% $200 10.00% $150 0.00% $100 -10.00% -20.00%

$0

-30.00% 1926 1930 1934 1938 1942 1946 1950 1954 1958 1962 1966 1970 1974 1978 1982 1986 1990 1994 1998 2002 2006 2010

$50

Figure 1.4: Steel price, percent change, and shocks by year

$300

80.00% Average Yearly Price

$250 $200

Yearly Percent Change

60.00%

Average Yearly Percent Change

40.00% 20.00%

$150 0.00% $100 -20.00% -40.00%

$0

-60.00% 1947 1950 1953 1956 1959 1962 1965 1968 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010

$50

Figure 1.5: Crude price, percent change, and shocks by year 7

Average Yearly Price

$250

25.00%

Yearly Percent Change

20.00% $200

Average Yearly Percent Change

15.00% $150

10.00% 5.00%

$100

0.00% $50 -5.00%

-10.00% 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011

$0

Figure 1.6: Cement price, percent change, and shocks by year

1.1.3 Risk Construction projects are exposed to various forms and degrees of risk, such as the previously identified price volatility and shocks. Within a project context, risk will be defined as the probability of an adverse or unfavorable project outcome that has the potential to diminish the likelihood of satisfying the project objectives or success criterion of the project. The E&C industry has tried to address risk through numerous approaches. In a project environment, participants, whether an owner or contractor, assume risk through contractual agreements. Each contractual agreement, through a selected project delivery method, allocates risk to one or more project participants. A common industry delivery method is lump-sum, where the contractor has assumed the materials price risk and will be paid a fixed 8

price no matter the actual cost of material. The uncertainty in actual costs opens the contractor to financial losses if the price dramatically increases. It also exposes the owner to a lost opportunity if the price dramatically decreases. Additional contractual clauses such as owner provided, price escalation, and force majeure are used to allocate materials. Thus, regardless of contractual agreement, project participants have a vested interest in managing the risk exposure of material. Contracts, by design, assign or transfer risk. Ideally, the risk would be assigned to the participant best equipped to control it. However, this has not been the case within the construction industry as demonstrated through the failure to meet financial expectations on projects and not properly addressing the market dynamics that impact material costs.

1.1.4 Traditional Mitigation Techniques A common mitigation technique is the allocation of additional financial resources through contingencies. Typically, monetary contingencies are a percent markup and subjective, and therefore do not capture the final cost. As shown in Figures 1.4, 1.5, and 1.6, if a materials price contingency is set equal to the average yearly percent change (APC) or other increments thereof, the contingency would have been insufficient to cover the materials price costs. Table 1.2 lists the insufficient contingency occurrences per commodity basis.

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Table 1.2: Calculated Data of Steel, Crude, and Cement STEEL Number of Years 85 Average Percent Change (APC) 4.03% Volatility 8.07%

Shocks

CRUDE CEMENT 64 40 7.74% 4.30% 21.55% 5.51% Occurrences 56 31 27 36 25 17 18 19 8 8 13 3 6 9 2 5 7 0 33.68% 69.51% 19.32%

>1% >APC > 2 X APC > 3 X APC > 4 X APC > 5 X APC Maximum

Source: U.S. Bureau of Labor (2012)

More than a third of the time, taking into consideration all three commodities, a contingency based on APC would not cover the cost escalation. Specifically, steel had 36 shocks above the average percent change out of 85 total occurrences, thus exceeding the contingency 42.35% of the time frame; crude had 25 out of 64 at 39.06%; and cement 17 of 40 at 42.50%. As evidenced by the data, the timing of purchasing decisions for raw materials with significant price fluctuations can greatly impact profits. The shocks were as extreme as 33.68% for steel, 69.51% for crude, and 19.32% for cement. This volatility is directly attributable to price fluctuations and indicates the criticality of decisions made for material procurement. Therefore, innovative risk mitigation techniques must be developed and implemented. This management dilemma provides the motivation for this research investigation.

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1.1.5 New Mitigation Techniques The use or implementation of financial tools, particularly derivatives, has been in use by other industries such as financial, agricultural, and manufacturing for decades and is rapidly spreading to and increasing in others. A derivative is a financial instrument whose value derives from the value of other, more basic, underlying resources. Through existing market exchanges, or clearing houses, the derivative provides an efficient mechanism to allocate materials price risk to a third party that has the capacity and desire to bear the risk as depicted in Figure 1.7. In doing so, bearers of the risk of contrasting exposure can utilize the marketplace to neutralize their risk.

Construction Participant

$ RISK

Clearing House

$ RISK

Bearer (Market

Participant)

Figure 1.7: Transfer of risk through derivative market The most common utilization of derivatives is through hedging. Hedging is a preventive measure that can be used by construction participants to protect their established materials price from adverse movements, i.e. escalation. Hedging simply takes the opposite position in the financial market compared to a participant’s exposure in the physical market; buying the underlying commodity of exposed material in a derivative to offset any potential increases from the initial circumstance in the physical. For example, buying what is needed in the future, in the form of a financial contract now, in order to sell it upon the purchase of the physical material

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later. This could be considered a form of insurance specific to the market or materials price risk mitigation.

1.2 PROBLEM STATEMENT Construction projects are too commonly over budget and must regularly contend with extreme fluctuations with regard to pricing of strategic materials. A resolution to the challenges of managing material pricing on construction projects is strategically necessary for project success. The E&C industry continues to struggle with effective cost control techniques and is in need of new tools and/or strategies to successfully address these mounting issues. Financial solutions to mitigate price risk are currently in use by other industries. An opportunity exists for the E&C industry to adopt these established strategies to improve project performance. However, the cost, complexity, and application of such strategies need to be understood. 1.3 RESEARCH SCOPE AND OBJECTIVES A research study was undertaken to investigate the benefits of risk mitigation techniques that diminish materials price fluctuations on construction projects. The scope encompasses any construction participant (i.e. owner or contractor), all sectors of the construction industry, and any scale of project, regardless of project delivery and contract type or location. The research objectives are as follows: 1. To identify existing strategies currently in use by the E&C industry as well as other

industries; 2. To demonstrate applicability and adaptability of existing financial solutions from other industries to the E&C industry on a project basis;

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3. To develop and propose a model for materials price risk mitigation using financial derivatives for use in the E&C industry; 4. To verify the positive, mitigating impact of the strategy using the proposed model. 1.4 RESEARCH METHODOLOGY The methodology necessary for accomplishing these stated objectives consists of seven steps, and is outlined in Figure 1.8. The first step required a comprehensive literature review of pertinent topical research areas. The second step was to further investigate established techniques of financial price mitigation employed by other industries facing similar risk. Techniques were explained and conveyed in greater detail for further understanding. Step three collected data from university sources that encompassed historical, current, and future commodity pricing of crude oil and steel derivatives. Additional data collection pertained to electric utility transmission projects posted in the public domain. Step four analyzed a financial hedging technique in simulated project durations. Standard financial calculations were used and statistical analysis performed for robustness. The fifth step developed a model for the E&C industry to guide participants through decisions and actions to successfully implement a hedge strategy appropriate for materials on a construction project. Step six involved exercising the model for validation. The seventh and final step drew necessary conclusions based on the results observed.

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Step 2 • Literature Review

Step 1

Step 4

• Data Collection • Commodity Prices • Transmission Projects

• Financial Risk Mitigation Techniques • Definitions, Explinations, Applications

• Caluclations, simulation, and statistical analysis

Step 3

Step 6 • Model development

• Execution and Validation of Model

• Draw conclusions • Provide recommedations

Step 5

Step 7

Figure 1.8: Summary of research methodology

1.5 OUTLINE OF DISSERTATION Chapter one provides an overview of the industry need, research problem, problem objectives, and methodology for materials price risk mitigation on construction projects. Chapter two describes pertinent concepts and definitions published in the literature of construction, engineering, and finance. The third chapter portrays the fundamentals of financial risk mitigation technique with a basic application. The fourth chapter specifically addresses objective two, the demonstration of applicability and adaptability of existing financial solutions. In this chapter, the data series used 14

is discussed as well as analyzed to show significance of applying financial risk mitigation techniques. First, statistics are presented to describe the characteristics of the data set. Second, a formula is defined as to how to analyze the data series over project sampling intervals. Finally, results are analyzed and discussed. The chapter is divided into three project duration time frames, analyzing oil and two steel commodities respectively. The fifth chapter presents the Financial Derivative Model for Construction Projects. This chapter addresses the third research objective. Chapter six is verification and validation of the proposed model. Case study applications of real world projects are simulated and reported. Lastly, chapter seven presents the conclusions and recommendations of this research. Limitations and future research are also included in this chapter.

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CHAPTER 2: Literature Review 2.1 INTRODUCTION This chapter will address the first objective of this research by identifying existing strategies in use by E&C industry, as well as other industries, by examining pertinent literature. The major topical areas investigated were uncertainties, risk, and finance. With a clear understanding of previous works, a new contribution can be made. This provides a solid foundation to undertake the analysis steps and provides the basis for the model development step which follows in subsequent chapters. 2.2 UNCERTAINTIES Even with adequate planning, the future remains uncertain to a degree because of external forces. The nature of construction project cycles creates a defined duration but is susceptible to forces outside of its control. Tseng et al. (2009) discussed uncertainties faced by construction companies such as uncertainty in costs, but more specifically how material costs are market driven and subject to volatility. Chua and Li (2000) found adequacy of resource market price information and resource price fluctuation to be significant to construction risk elements. Ling (2005) found that the reliability of company pricing is a global risk factor with significant fiscal impact. Similarly, Oberlender and Trost (2001) identified cost information applicability and completeness as major factors in estimate quality.

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Recognition of the significant impacts that

market condition awareness and volatile material pricing can have on project success is an insight that the entire E&C industry must have in today’s global economy. Mochtar and Arditi (2001) proposed pricing strategies centered on a market-based approach for the U.S. construction industry after confirming that current strategy is predominantly cost-based. Taking a historical perspective, the industry has frequently experienced periods of considerable price escalation or high levels of volatility. Williams (1980) noted the significant impacts of cost inflation on project estimating during the unprecedented escalation in material costs during the period of 1973-76. The sensitivity to market conditions is obvious. Such trends, or specifically the E&C industry’s susceptibility to economic influences, will not abate in the future and may, in fact, have more impact than ever before. Prices may fluctuate for a variety of factors, including exchange rate movements, supply stream/transportation issues, commodity market structure, public information, inflationary conditions, technology, innovations, regulatory law, world events, politics, environment, and customer preferences. The high probability of cost overruns creates significant financial risk to project participants, contractors and owners alike (Akinci and Fischer, 1998; Baloi and Price, 2003). Since these factors will occur to varying degrees on projects, frequently impacting project performance, the overall effectiveness of risk management programs must become an important concern in all project related endeavors (Wang and Chou, 2003). 2.3 RISK For an E&C industry in a project setting, risk can be defined as the probability of an adverse event occurring during the project life cycle. The understanding of the need to manage risk is clearly noted and effective strategies are continually advanced. Project Management Institute (2008) defined risk management as the systematic process of identifying, analyzing, and 17

responding to the project risk. Similarly for the Construction Industry Institute (2013), a total risk management program has three stages: risk identification, risk measurement, and risk control. Identifying risk is the initial action required and is project specific in scale but involves major themes that occur across the industry, particularly financial ones. Measuring and analyzing risk is abundant in literature with varying methods according to type and approach. A summary of tools and techniques frequently appearing in construction risk management literature, provided by Dey et al. (2002), is shown in Table 2.1. Risk control and response is the process of defining alternatives and taking actions to reduce influence to the project outcome. Risk control and response employ one or more of the techniques of avoidance, transference, mitigation and acceptance.

Table 2.1: Summary of Construction Risk Management Tools and Techniques Method

Keynotes

Influence diagram

-Risk identification -Brain storming & Delphi technique -Relationship of variables Monte -Distribution form Carlo -Variables’ Simulation correlation (MCS)

PERT

(Same as above) -network scheduling Sensitivity -Deterministic analysis -Variables’ correlation

Application and Previous Study Who & When Topic Ashley and Bonner Identification of political risks in (1987) international project Yingsutthipun Identification of risks in (1998) transportation project in Thailand Songer, Diekmann and Pecsok (1997) Chau (1995) Wall (1997)

Debt cover ratio (project cash flow) in a toll way project Distribution form for cost estimate Distribution form and correlation between variables in building costs Dey and Ogunlana Project time risk analysis through (2001) simulation Hatush and Skitmore Contractor’s performance estimate (1997) for contractual purpose Yeo (1990) Probabilistic element in sensitivity Yeo (1991) analysis for cost estimate Woodward (1995)

18

Survey on use of sensitivity analysis in BOT project in UK

MCDM

-Multi-objective -Subjectivity

AHP

-Systematic approach to incorporate subjectivity -Consistency of judgment

Moselhi and Deb (1992) Dozzi, AbouRizk and Schroeder (1996) Dey, Tabucanon and Ogunlana (1994) Mustafa and AlBahar (1991) Zhi (1995) Nadeem (1998)

Fuzzy set approach (FSA)

-Vagueness of subjective judgment

Kangari and Riggs (1989) Diekmann (1992) Lorterapong and Moselhi (1996)

Neural network approach (NNA) Decision tree Fault tree analysis Risk checklist Risk mapping Cause/effe ct diagram

-Implicit relationship of variables

-Expected value -Accident analysis -Safety management -From experiences -Two dimensionality of risk -Risk identification

Delphi -Subjectivity technique Combined -Probability, severity AHP and and expected Decision monetary value tree Source: Dey et al. (2002)

Paek, Lee and Ock (1993) Chua, et al. (1997) Boussabaine and Kaka (1998) Haimes, et.al (1989)

Project alternative selection under risk Bid mark-up decision making

Risk analysis for contingency allocation Risk analysis for international construction project Risk analysis for oversea construction project Risk analysis for BOT project in Pakistan Risk assessment by linguistic analysis Combination of influence diagram with fuzzy set approach Network scheduling by fuzzy set approach Risk pricing in construction project through fuzzy set approach Development of budget performance model Cost flow prediction in construction project Multi-objective decision tree

Tulsiani, et al. (1990) Perry and Hayes (1985) Williams (1996)

Risk evaluator

Dey, 1997

Symbiosis of organizational reengineering and project risk management for effective implementation of projects Same as above

Dey, 1997 Dey, 2001b

19

Risk and its management in construction project Two dimensionality of project risk

Decision support system for risk management

Of particular interest for the proposed research in this study is the category of financial risk, and more particularly project costs. Risk strategy techniques for project costs can be placed into the three broad phases of a risk management process: (1) risk identification factors including procurement of materials (Akinci and Fischer, 1998; Baloi and Price, 2003; Creedy et al. 2010; Flyvbjerg et al., 2003; Hastak and Shaked, 2000; Jaafari, 2001; Jergeas and Ruwanpura, 2010; Kangari, 1995; Shane et al., 2009; Wilmot and Cheng, 2003; Zhi, 1995); (2) risk measurement of factors and techniques (Dozzi and AbouRizk, 1996; Mak and Picken, 2000; Panthi et al., 2009; Thal et al., Touran, 20032010; Yeo, 1990;); and (3) risk mitigation (Nagashima et al., 2011; Tseng et al., 2009). The studies cited above, along with previously mentioned ones, aid project participants by providing techniques for the identification and measurement of risk. Successful, real world strategies for risk mitigation, however, are much more difficult to find in project literature. The following discussion highlights what is currently in practice in the E&C industry and what is established in other industries. 2.5 TRADITIONAL CONSTRUCTION MITIGATION A common risk response strategy is the use of contingency provisions. A contingency is the part of a budget intended for uncertainties around potential costs to mitigate the risk of overrun on a project. Some contingency costs may be included for unanticipated price changes and underestimates the costs and quantities (Patrascu, 1988). Thomson and Perry (1992) explain contingency as “an allowance added to an estimate to represent the best judgment of undefined or uncertain items of work which it is considered should be provided for.” Contingency directly influences project awards and outcome. If a contingency is too much, it may render a good proposal unacceptable and if it is too little, it may make it an unrealistic endeavor (Wideman, 20

1995). The amount of contingency and its owner or use is often unclear and arbitrary (Patrascu, 1988). It can be for materials, weather, schedule, or other factors. It is often calculated as a percent markup to the overall estimate or portioned to line-items on the estimate. This fails to properly account for specific material costs as estimated versus as they are actually procured. Additional strategies for materials have also been developed and employed. Weidman et al. (2011) found several alternatives in practice to allocate risk associated with materials price fluctuations, such as stockpiling, contractual clauses for escalation, timely buyout, communications, and time stipulations on bids. Yet these methods also fail to capture the final or actual cost of a material at the time of need. Furthermore, such existing strategies are not always adopted, or if adopted do not adequately control material costs, thereby contributing to the failure of meeting project financial expectations in the E&C industry. 2.4 OTHER INDUSTRIES MITIGATION New tools and innovative financial strategies for dealing with the above identified risks are now available and have successfully been employed by other industries. One such important financial strategy that has proven effectiveness in other industries is referred to as hedging, or the use of derivative instruments. The use of this strategy to mitigate risk is well established and has been successfully implemented in a variety of applications. Bartram et al. (2009) showed the use of commodity price derivatives is globally concentrated in the utilities, oil, mining, steel, chemicals, food, and transportation industries. Certain financial literature provided a basis for understanding the use of hedge for industries outside of the financial sectors uses of hedges, yet does not focus on whether hedging achieves reasonable economic objectives (Allayannis and Ofek, 2001; Berkman and Bradbury, 1996; Dolde, 1995; Gay and Nam, 1998; Géczy et al., 1997; Graham and Rogers, 2002; Haushalter, 2000; Mian, 1996; Nance et al., 1993; Rogers, 21

2002; Schrand and Unal, 1998; and Tufano, 1996). The Nguyen and Faff (2003) survey of firms in Australia found that derivative usage and attitude towards such usage is industry biased. Similar results were found in a study conducted by Geczy et al. (1997) of Fortune 500 companies. Particular examples of hedging use can be found in the airline and electric energy sectors. Various airlines have used this strategy to avoid swings in jet fuel pricing through hedges in oil contracts. This enables them to set a ceiling price for their fuel, however it prevents them from gaining when prices decrease. This loss opportunity can be very disadvantageous due to the competitive airline market (Carter et al., 2006). Similarly electric utilities have used hedges to limit their exposure to increasing fuel prices as well as to set minimum price for the electricity they produce (Bjorgan et al., 1999). Most major companies have their own clearing houses to handle all of these transactions. A goal for the E&C industry is to mitigate materials price risk according to financial expectations per project. The E&C industry would benefit by adopting these new strategies and use them to diminish their exposure to the negative consequences of materials price risk. Dozzi and AbouRizk (1996) found in surveys that most construction participants are risk averse and that contracts apportion risk unfairly to contractor and subcontractor rather than or compared to owners or consultants. Certain sectors, such as transportation highway (Damnjanovic and Zhou 2009) and material suppliers (Chen and Lin 2010), have started to investigate their usage in the construction environment. 2.5 SUMMARY The relevant literature for this research established the uncertainty of material costs on projects and the need for effective risk management and control. The literature further explored 22

current strategies in the E&C industry that do not adequately mitigate the risk and identified a new technique of hedging that is utilized by other industries. New contributions can be made to the E&C industry through development of the concept and application of hedging on construction projects.

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CHAPTER 3: Financial Risk Mitigation Technique

3.1 DESCRIPTION A derivative is a financial instrument whose value is based on the value of other, more basic, underlying resources. The derivatives are created from the prices of traded commodities which occur in various markets. An over-the-counter (OTC) market consists of traders who are usually representatives of financial institutions, corporations, and fund managers that deal interpersonally. Alternatively, exchange-traded markets in existence throughout the world since mid-1800s consist of individual participants trading standardized contracts that are defined by each exchange (Williams, 1982). These markets further establish a place for the transfer of risk, where some participants wish to avoid risk, others deliberately want to acquire it. Use of derivative contracts that reflect the underlying resource of the required material should effectively reduce exposure to price escalation of the material. 3.2 EXPLANATION A hedge is simply a trade of derivatives designed to reduce risk. Hedging offsets possible risk in one market through the deliberate assumption of an approximately equal and opposite risk in another market. Because the E&C industry is in the business of providing services on major construction projects, it lacks the particular expertise in predicting commodity prices. Yet the E&C industry is greatly influenced by commodity prices and might benefit favorably by hedging certain volatile commodities. A properly utilized hedge strategy could 24

protect the E&C industry from upside exposure in prices yet limit downside windfall. However, this is not a concern since the aim is to meet the estimated cost or budget line item and not to profit from the commodity price changes. Some strategies allow for downside windfall capture as well. Certain hedges known as “swaps,” “forwards,” “futures,” and “options” are illustrated in Figure 3.1 and will be discussed in greater detail (Hull, 2009). It is important to note that the pertinent forward and futures for a construction project is the long hedge.

Figure 3.1: Depiction of four common hedge strategies applicable for the E&C industry 25

3.2.1 Swap The “swap” is a simple and basic agreement whereby a floating price is exchanged for a fixed price over a certain period of time or vice versa. The swap contract will specify the volume, the duration, and the fixed and floating prices for the commodity. The differences between fixed and floating prices are settled in cash for specific predetermined periods. It is an off-balance-sheet financial arrangement, which involves no transfer of the physical item. Both parties settle their contractual obligations by means of a transfer of cash. A basic swap is depicted in Figure 3.2.

Figure 3.2: A common hedge strategy, swap 3.2.2 Forward A “forward” is an agreement to buy or sell an asset at a certain time in the future for a certain price. These contracts are traded in OTC markets. In Figure 3.3, a long hedge is depicted where the buyer has a long position, agreeing to make delivery of the commodity, while the seller has a short position, agreeing to make delivery of the commodity.

26

Figure 3.3: A common hedge strategy, forward 3.2.3 Futures Similar to forwards, “futures” are another financial risk management tool that enables companies to hedge their price risk exposure by agreeing to buy or sell a particular volume of product for delivery on a fixed future date at a price agreed today. Futures are traded on an exchange, which specifies standard terms for the contracts and guarantees the performance, thereby removing counter-party risk. Generally, positions are closed out before the contract delivery date by entering into the opposite position. In reality, only a small percentage of futures results in actual delivery of the commodity. Instead, it is a financial transaction. 3.2.4 Option An option is the right to buy or sell an asset. More specifically a call option is the right to buy a particular asset at a predetermined fixed price (the strike) at any time up until the maturity date. A call option protects your business from an increase in market movement, depicted in Figure 3.4. The buyer pays a premium for the right to collect monies if the excess occurs. The other side is a put option, which pays the buyer in the event of a downward market movement.

27

This would not have direct application on a construction project and would be considered speculative or solely profit driven by a participant.

Figure 3.4: A common hedge strategy, call option Lastly, a collar is a combination of a put option and a call option. For a participant planning to purchase a commodity, a collar is created by selling a put option with a strike price below the current commodity price and purchasing a call option with a strike price above the current commodity price. The purchase of a call option provides protection during the life of the option against upward commodity price movements above the call strike price. The premium received from selling the put option helps offset the cost of the call option. By establishing a collar strategy, a minimum and maximum commodity price is created around a hedger’s position until the expiration of the options. A collar can be structured so that the premium received from the sale of the put option completely offsets the purchase price of the call option. This type of collar is called a “zero cost collar.” If additional protection against upward price movements is warranted or vice versa, a premium collar is used. With a premium collar, see Figure 3.5, the cost of the call option is only partially offset by the premium received from selling a put option. 28

Figure 3.5: An advanced hedge strategy, premium collar 3.2.5 General The use of hedging comes with associated costs in the form of mandated margins, premiums, or fees. Companies also need to be aware of related strict accounting measures as well as how it may impact their income statement and balance sheet. Regardless, hedges are an appropriate mitigation strategy for price risk and should be considered for use on construction projects. The E&C industry also needs to identify and understand these risks through proper indices, data, and analysis. When the analysis shows that the risk can be effectively hedged, then the next step is to transfer the identified risk in a cost effective manner through the implementation of a financial contract. Now a case study will be presented using the commodity of steel to show the applicability and adaptability hedging steel prices in construction projects. 3.3 CASE STUDY APPLICATION A renaissance is occurring in the electric utility industry that can be attributed to population growth and ever increasing demand for electric power. The demand for electrical power requires additional capital expenditures in infrastructure, specifically in construction of 29

transmission lines. According to findings by American Electric Power Company (2012), new construction of a 765 kV transmission line would be upwards of $4 million per mile. Materials comprise approximately 41% of the total cost with the structures being 60% of the materials cost. This project will be used to illustrate the useful benefit of financial derivatives described above. A line of 250 miles was proposed in January 2006 in the Northeast U.S. and a joint venture was established to perform the work in April 2007, all to meet an in-service date of June 2012 (EEI, 2012). These structures required 5500 steel units at an estimated cost of $185 per unit. Various scenarios using derivatives will be presented in contrast to current common practices. Scenario 0 shows the current practice of not hedging. Scenario 1 illustrates a swap, Scenario 2 illustrates a futures contract, Scenario 3 illustrates a call option, and Scenario 4 illustrates a premium collar. Figure 3.6 depicts steel price movements for the case study project’s duration. $350

Case Study

$300 $250 $200 $150 $100 Average Monthly Steel Price $50 Jan-06 Mar-06 May-06 Jul-06 Sep-06 Nov-06 Jan-07 Mar-07 May-07 Jul-07 Sep-07 Nov-07 Jan-08 Mar-08 May-08 Jul-08 Sep-08 Nov-08 Jan-09 Mar-09 May-09 Jul-09 Sep-09 Nov-09 Jan-10 Mar-10 May-10 Jul-10 Sep-10 Nov-10 Jan-11 Mar-11 May-11 Jul-11 Sep-11 Nov-11 Jan-12 Mar-12 May-12

$0

Figure 3.6: Steel prices for the entire duration of the case study Source: U.S. Bureau of Labor 30

In Scenario 0, the current common practice (i.e., no usage of derivatives), steel materials would have been purchased after project conception and initiation around June 2008. The price for a unit of steel at that date would have been $284.10, resulting in project steel cost of $1,136,400. For Scenario 1, a swap is executed where the project participant pays a fixed price and receives a floating price, both indexed to the expected steel product use during each settlement period. The volume of steel hedged is negotiable because this is a customized contract. During the life of the swap contract, the participant buys steel in the cash market, as usual, but the swap contract makes up the difference when prices increase and removes the difference when prices fall. The result for the participant is a fixed price for the contracted period. The fixed rate payment is set based on market conditions when the swap contract is initiated. The floating price of steel could be based on established exchanges and calculated monthly using daily prices for the month. The net monthly payment to the construction participant is the floating rate minus the fixed rate. This continues for the duration of the contract. By being the fixed-price payer, the participant has hedged the steel price risk at a fixed amount. Ideally this price is the estimated amount for the project. For this case study project, the pre-determined fixed rate is $185.00 per unit and the floating rate will be used for January 2006 to June 2008. The monthly settlements are provided in Table 3.1.

31

Table 3.1: Monthly Swap Settlements for Scenario 1 RATES FIXED

FLOATING

Jan-06 Feb-06 Mar-06 Apr-06 May-06 Jun-06 Jul-06 Aug-06 Sep-06 Oct-06 Nov-06 Dec-06 Jan-07 Feb-07 Mar-07 Apr-07 May-07 Jun-07 Jul-07 Aug-07 Sep-07 Oct-07 Nov-07 Dec-07 Jan-08 Feb-08 Mar-08 Apr-08 May-08 Jun-08

PRICES 185.00 174.7 177.2 178.1 180.7 183.7 189 193.9 192.2 195.3 194.9 189 189.6 190 195.9 205.5 209.2 205.8 205.3 204.3 200.5 199.8 198 197.8 200.6 210 215.2 224 250.6 270.6 284.1

SETTLEMENT -10.30 -7.80 -6.90 -4.30 -1.30 4.00 8.90 7.20 10.30 9.90 4.00 4.60 5.00 10.90 20.50 24.20 20.80 20.30 19.30 15.50 14.80 13.00 12.80 15.60 25.00 30.20 39.00 65.60 85.60 99.10

Since the size of the contract is 5500 units for June 2008, a payment of $545,050 is received by the project participant. A project participant uses an established futures contract to hedge project steel price risk for Scenario 2. Since the construction participant is short the physical commodity, the long 32

position in the futures market is taken. The participant, or hedger, purchases a futures contract at $185.00 per unit, with a lot size of 5500 units in January 2006. On the same day, the spot price is $174.70 per unit. If the hedger closes out this futures contract for 5500 units in June 2008 at $290, they get a profit of $577,500. The spot price of steel on the settlement date in June is $284.10, which would have required a non-hedged participant to pay $109.40 per unit more for the same product. Yet, by using the futures contract and then purchasing steel in the spot market, the gain of $577,500 on the futures offsets the $109.40 increase in steel prices. In essence, the hedger’s net cost of steel is $179.10 per unit. The E&C industry might find value in the flexibility that options provide, but it comes with a price in the form of a premium. The premium price is greatly affected by the volatility of the underlying commodity; as volatility increases so does the premium. For this reason, collars are often used for volatile commodities that are committed for in the present and acquired in the future. Using a collar may be a reasonable hedging strategy for the construction participant since it involves little to no upfront cost and involves no speculative return. This is implemented by the premium paid for the call option portion that is reduced by the amount of the premium collected by the put option portion. In essence, sharing the potential profit windfall of low prices reduces the premium for high price protection. However, if steel prices dramatically decrease, the hedger may pay more for steel and miss out on possible additional profits, but if properly tied to a project budget it will meet the contractual commitment. For Scenario 3, a call option is placed at $180.00 with a $5 per unit premium. As of June 2008, the spot price is above the call, therefore the option would be exercised and an overall purchase price equals $185. A collar is created in

33

Scenario 4 by placing a call option at $184.00 and a put option at $170 with a premium of $1. Similarly to Scenario 3, an overall purchase price of $185 is reached. Figure 3.7 provides a conceptual illustration for hedging gains or losses using swaps, futures, call options, and premium collar when locking into a $185 per unit price of steel.

Figure 3.7: Depiction of four hedge strategies for the case study of steel The lightly shaded areas in Figure 3.7 reflect the hedging gains for each scenario and the darker shaded regions the hedging losses. It is important to note that a gain is protection from an 34

adverse upward movement in market pricing while a loss is not financial but rather the lost opportunity of the downward price movement. In the following Table 3.2, a summary of the scenarios is provided showing the prices, costs, and potential project savings.

Table 3.2: Summary of Scenarios and Project Cost Impact.

Cost per unit Total Cost Savings

SCENARIOS 0 1 2 3 4 $284.10 $185.00 $179.10 $185.00 $185.00 $1,562,550.00 $1,017,500.00 $957,550.00 $1,017,500.00 $1,017,500.00 ($545,050.00) $0.00 $32,450.00 $0.00 $0.00

All hedge Scenarios 1-4, satisfy steel material costs thus meeting project expectations. Further, Scenario 2 was actually under projected costs. However, for Scenario 0, the project costs escalates by more than 33%, which typical mark-up strategies would not cover, leaving the project participant financially exposed. The magnitude of exposure to steel price risk will affect the participant's hedging decisions, and in turn, hedging decisions affect exposure to price fluctuations. 3.4 SUMMARY In the E&C industry each project is unique and has a defined budget, materials, and contract. These factors lend to one of the project participants exposed to the materials price risk. This risk has been identified, measured, and mitigated in the past but to a limited effect. Traditionally, this risk has been mitigated through allocation or acceptance of this risk to the contractor, often leading to poor project performance by not meeting success criteria. Hedging provides a new method that can neutralize the risk of materials price escalation. A properly used hedge on a project will help meet the success criteria as the analysis will show in Chapter 4.

35

CHAPTER 4: Statistical Analysis of Applied Hedging Strategies

4.1 INTRODUCTION As previously discussed, hedging is a proactive technique to mitigate certain financial risk. Specifically, it is believed that the use of hedges will effectively reduce exposure to the negative effects of price volatility. In order to statistically analyze this mitigation technique, a simulation was performed using the “futures” application of hedging illustrated by Scenario 2 in Chapter 3. The selection of futures was made for the simplicity of calculation and transparent nature of the trading market. Additionally, futures have commonality with other types of derivatives providing for inference thorough substitution of other derivative types as demonstrated in Chapter 3. The goal of this statistical analysis is to determine the effectiveness of hedging as a strategy to mitigate or reduce financial risk resulting from price escalation over a project’s duration. A statistical software package by Minitab, Inc., accessed through the University of Alabama, is utilized for statistical analysis and documentation (Minitab, 2007). It was selected for its userfriendless, availability, and capabilities. It could perform the necessary calculations that are defined in the following discussion and provide visuals in various graphical formats. Particularly histograms and boxplots are shown for their simplicity and ease of interpretation. Histograms show the frequency of occurrence per data set and Boxplots depict quartiles. Since the spread of the data sets vary significantly, boxplots are best suited for comparison (Williamson et al., 1989).

36

4.2 DATA COLLECTION The data required for analysis are historical spot prices and prices of futures contracts pertaining to commodities of interest, i.e. oil and steel. In this research, the actively traded derivative that will be used for investigation of oil is West Texas Intermediate (WTI) crude oil, and the actively traded derivatives for steel are London Metal Exchange Steel Billet (LME) and U.S. Midwest Domestic Hot-Rolled Coil Steel Index Futures (HRC). The data was obtained through two sources: U.S. Energy Information Administration for WTI and Bloomberg Markets for LME and HRC (Commodities, 2013; U.S. EIA, 2012).

Due to the proprietary nature of

Bloomberg Markets and the University of Alabama’s contract allowing access to the information but not to publish, only WTI prices are provided in Appendix A. The ticker symbol utilized for LME spot price is LMFMDY that represents LME Steel Billets Cash, and with LMFMDS03 that represents LME Steel Billets 3 Month Rolling Forward used for LME future price. Similarly for HRC, the tickers used are MBST5274 (Index on Iron & Steel Metal Bulletin’s appraisal of HRC price in US) for spot price and HRC1 (Generic 1st HRC Future) for future price. All prices are in daily price format except for MBST5274, the HRC spot price, which is in a weekly price format. The prices were collected for the time period each derivative had been available for trading through the end of day on March 12, 2012.

Table 4.1 provides the specific time periods and

number of data points available per derivative. Since WTI has been traded for a longer period of time, more daily prices were available than the newer derivatives of LME and HRC.

37

Table 4.1: Time Periods and Number of Data Points Commodity

Time Period

WTI LME HRC

January 1986 – March 2013 July 2008 – March 2013 October 2008 – March 2013

Number of Spot Prices 6859 1171 227

Number of Futures Prices 6859 1171 1104

These daily prices were averaged over each month in the time period to establish a contract price. This contract price is not necessarily indicative of a price available on a certain day, but instead it is reflective of the possible price for the entire month. This method was used rather than the price at the close of a month to accommodate fluctuations that may occur during a given month. The graphs of the monthly spot and futures pricing are shown in Figures 4.1, 4.2, and 4.3. The similarity of the two prices for WTI reflects the large volume of trades while LME and HRC have more disparity, possibly due to less volume and novelty of product.

$160 $140 $120

Spot Prices

$100

Futures Prices

$80 $60 $40 $20 Jan-1986 Mar-1987 May-1988 Jul-1989 Sep-1990 Nov-1991 Jan-1993 Mar-1994 May-1995 Jul-1996 Sep-1997 Nov-1998 Jan-2000 Mar-2001 May-2002 Jul-2003 Sep-2004 Nov-2005 Jan-2007 Mar-2008 May-2009 Jul-2010 Sep-2011 Nov-2012

$0

Figure 4.1: WTI monthly spot and futures pricing 38

$1,200 $1,000

Spot Prices Futures Prices

$800 $600 $400 $200

Figure 4.2: LME monthly spot and futures pricing

$1,200 Spot Prices $1,000

Futures Prices

$800 $600 $400

$200 $0

Figure 4.3: HRC monthly spot and futures pricing

39

Jan-2013

Oct-2012

Jul-2012

Apr-2012

Jan-2012

Oct-2011

Jul-2011

Apr-2011

Jan-2011

Oct-2010

Jul-2010

Apr-2010

Jan-2010

Oct-2009

Jul-2009

Apr-2009

Jan-2009

Oct-2008

Jul-2008

$0

4.3 SIMULATION A simulation was performed to evaluate the benefit of implementing a futures hedging strategy. For any instance there were two actions available; hedge or not to hedge. Hedging, by definition, was participating in the futures market to the fullest extent possible at project initiation and conclusion, while not hedging was to purchase only at the concluding price. Since not hedging was the common operating procedure currently in use, it will be the baseline of comparison for purchasing materials at the spot price at the end of the project period. To further reflect the different project durations that occur, various coverage lengths were analyzed as deemed representative for the nature of construction projects and the evolution of estimating cycles. Up to eight coverage lengths were calculated for the derivative for each type of material. For example, a short duration would be one month from project initiation to material purchase conclusion. In order to simulate the standard action for WTI, the difference between the spot prices of each month was determined starting with January 1986 and February 1986, continuing until February 2013 and March 2013. For hedging, the simulation further incorporated the January 1986 and February 1986 futures price difference continuing until February 2013 and March 2013. The same simulation occurs for LME and HRC, except for the different starting months of July 2008 and October 2008, respectively. As the coverage length changes progress over time and from short to long project durations, the span between the initiation and conclusion changes. An example for long duration was five years and was only experienced by WTI due to its longer history of trading. The standard action for such long duration started with the difference between January 1986 and January 1991 spot prices and then continued for the entire time period. The hedge action also included the difference between January 1986 and January 1991 futures prices until the final comparison 40

between the spot prices of March 2008 and March 2013. Table 4.2 shows a snapshot of the first three comparison dates and last date for each materials derivative over each coverage length investigated.

Table 4.2: Snapshot of First Three Comparison Dates and Last Date for Each Coverage Length Investigated Coverage Derivative 1st 2nd 3rd Final Length Jan 1986 – Feb 1986 – Mar 1986 – Feb 2013 – 1M Feb 1986 Mar 1986 Apr 1986 Mar 2013 Jan 1986 – Feb 1986 – Mar 1986 – Dec 2012 – 3M Apr 1986 May 1986 Jun 1986 Mar 2013 Jan 1986 – Feb 1986 – Mar 1986 – Sep 2012 – 6M Jul 1986 Aug 1986 Sep 1986 Mar 2013 Jan 1986 – Feb 1986 – Mar 1986 – Mar 2012 – 12 M Jan 1987 Feb 1987 Mar 1987 Mar 2013 WTI Jan 1986 – Feb 1986 – Mar 1986 – Sep 2011 – 18 M Jul 1987 Aug 1987 Sep 1987 Mar 2013 Jan 1986 – Feb 1986 – Mar 1986 – Mar 2011 – 2Y Jan 1988 Feb 1988 Mar 1988 Mar 2013 Jan 1986 – Feb 1986 – Mar 1986 – Mar 2010 – 3Y Jan 1989 Feb 1989 Mar 1989 Mar 2013 Jan 1986 – Feb 1986 – Mar 1986 – Mar 2008 – 5Y Jan 1991 Feb 1991 Mar 1991 Mar 2013 Jul 2008 – Aug 2008 – Sep 2008 – Feb 2013 – 1M Aug 2008 Sep 2008 Oct 2008 Mar 2013 Jul 2008 – Aug 2008 – Sep 2008 – Dec2012 – 3M Oct 2008 Nov 2008 Dec 2008 Mar 2013 Jul 2008 – Aug 2008 – Sep 2008 – Sep 2012 – 6M Jan 2009 Feb 2009 Mar 2009 Mar 2013 Jul 2008 – Aug 2008 – Sep 2008 – Mar 2012 – LME 12 M Jul 2009 Aug 2009 Sep 2009 Mar 2013 Jul 2008 – Aug 2008 – Sep 2008 – Sep 2011 – 18 M Jan 2010 Feb 2010 Mar 2010 Mar 2013 Jul 2008 – Aug 2008 – Sep 2008 – Mar 2011 – 2Y Jul 2010 Aug 2010 Sep 2010 Mar 2013 Jul 2008 – Aug 2008 – Sep 2008 – Mar 2010 – 3Y Jul 2011 Aug 2011 Sep 2011 Mar 2013 Oct 2008 – Nov 2008 – Dec 2008 – Feb 2013 – 1M Nov 2008 Dec 2008 Jan 2009 Mar 2013 HRC Oct 2008 – Nov 2008 – Dec 2008 – Dec 2012 – 3M Jan 2009 Feb 2009 Mar 2009 Mar 2013 41

6M 12 M 18 M 2Y 3Y

Oct 2008 – Apr 2009 Oct 2008 – Oct 2009 Oct 2008 – Apr 2010 Oct 2008 – Oct 2010 Oct 2008 – Oct 2011

Nov 2008 – May 2009 Nov 2008 – Nov 2009 Nov 2008 – May 2010 Nov 2008 – Nov 2010 Nov 2008 – Nov 2011

Dec 2008 – Jun 2009 Dec 2008 – Dec 2009 Dec 2008 – Jun 2010 Dec 2008 – Dec 2010 Dec 2008 – Dec 2011

Sep 2012 – Mar 2013 Mar 2012 – Mar 2013 Sep 2011 – Mar 2013 Mar 2011 – Mar 2013 Mar 2010 – Mar 2013

Calculations were necessary to perform the simulation. The effective price when hedging is determined by the equation provided below for the Effective Price (Hull, 2009). –

–

(Eq. 4.1)

Define S : Asset price at time of initiation 0

S : Asset price at time of conclusion or purchase of material t

F : Futures price at time of initiation or hedge is set up 0

F : Futures price at time of conclusion or material is purchased t

For analysis of the two actions, Hedge (H) and Standard (S) the following equations were used: (Eq. 4.2, 4.3) Both actions are presented as percentage change due to varying consumption requirements on construction projects and to normalize derivative types. Using Minitab (2007), Figures 4.4 and 4.5 show the summary of the simulated actions either Hedge (H) or Standard (S), including distributions, for one month coverage length for WTI. See Appendix B for the summary of all other coverage lengths and derivatives. 42

Figure 4.4: Summary of the simulated actions for one month WTI using hedge

Figure 4.5: Summary of the simulated actions for one month WTI using standard 43

4.4 FINDINGS Construction projects are unique with vastly different durations. The importance of the simulation is to determine if hedging is beneficial regardless of commodity or project duration. Short durations will be considered 1 month (1M), 3 months (3M), and 6 months (6M), with outage and maintenance projects, or renovation and revamp projects, being examples of these type that can and do occur. Commercial and multi-family residential projects are generally medium durations of 12 months (12M) and 18 months (18M). Heavy civil, infrastructure and industrial projects typically have long durations and will be considered 2 years (2Y), 3 years (3Y) and 5 years (5Y) for this simulation. Common statistics were determined with additional calculations for percent effective and range. For a hedge strategy to be considered beneficial the mean should be near zero or closer to zero, with a smaller range and less variability than the standard action.

4.4.1 Short Duration The analysis results for the short duration actions, hedge and standard, are shown in Table 4.3. This section breaks down the data by 1M, 3M and 6M coverage lengths. For all materials and every coverage length the hedge was effective more than 70% of the time; this means that the calculated hedge value was greater than the standard value or greater than zero.

44

Table 4.3: Analysis Results for Short Duration Actions

4.4.1.1 One Month The box plot for 1M actions, hedge and standard, is depicted in Figures 4.6 – 4.8, for WTI, LME, and HRC respectively. For this coverage length, the hedged mean yield of WTI is -0.01% with a range of 4.07%, while comparatively the standard averaged at -0.80% but with a range of 80.6%. Conversely for LME, the mean yield for hedging is lower at 0.14% than the standard at 1.98%, yet the range is smaller at 16.54% for hedging than 59.65% for the standard. HRC’s mean yield is 0.93% greater for hedging while the standard is 0.52%. Again the range remains smaller for hedging, 31.97%, compared to the standard at 45.54%.

45

Figure 4.6: One month hedge and standard actions for WTI

Figure 4.7: One month hedge and standard actions for LME 46

Figure 4.8: One month hedge and standard actions for HRC 4.4.1.2 Three Months The result of the analysis for 3M actions, hedge and standard, is depicted in Figures 4.9 – 4.11, for WTI, LME, and HRC respectively. For this coverage length, the hedged mean yield of WTI is -0.01% with a range of 4.20%, while comparatively the standard averaged at -3.08% but with a range of 161.16%. Conversely for LME, the mean yield for hedging is lower at 0.55% than the standard at 3.71%, yet the range is smaller at 17.08% for hedging than 98.10% for the standard. HRC’s mean yield is 2.10% for hedging and the standard is 0.32%. Again the range remains smaller for hedging, 60.67%, compared to the standard at 97.13%.

47

Figure 4.9: Three months hedge and standard actions for WTI

Figure 4.10: Three months hedge and standard actions for LME 48

Figure 4.11: Three month hedge and standard actions for HRC

4.4.1.3 Six Months The result of the analysis for 6M actions, hedge and standard, is depicted in Figures 4.12 – 4.14 for WTI, LME, and HRC respectively, and is similar to the results for the 1M and 3M durations. For this coverage length, the hedged mean yield of WTI is -0.02% with a range of 6.12%, while comparatively the standard averaged at -6.29% but with a range of 164.84%. Conversely for LME, the mean yield for hedging is lower at 0.81% than the standard at 3.16%, yet the range is smaller at 18.20% for hedging than 119.46% for the standard. HRC’s mean yield is 2.41% for hedging while the standard is -1.77%. Again the range remains smaller for hedging, 55.99%, compared to the standard at 108.80%.

49

Figure 4.12: Six months hedge and standard actions for WTI

Figure 4.13: Six months hedge and standard actions for LME 50

Figure 4.14: Six months hedge and standard actions for HRC 4.4.1.4 Summary For the short durations, the data shows that the mean for hedging is close to 0 while the mean for standard deviates farther, especially for WTI. The range is always larger for the standard than the hedge with the difference increasing over coverage length. For side-by-side comparison of short duration coverage lengths per derivative see Figure 4.15.

51

HEDGE

STANDARD

Figure 4.15: Comparison of two actions for WTI, LME and HRC on short durations 4.4.2 Medium Duration The analysis results for the medium duration actions, hedge and standard, are shown in Table 4.4. This section breaks down the data by 12M and 18M coverage lengths. Table 4.4: Analysis Results for Medium Duration Actions

52

4.4.2.1 Twelve Months The result of the analysis for 12M actions, hedge and standard, is depicted in Figures 4.16 – 4.18, for WTI, LME, and HRC respectively. For this coverage length, the hedged mean yield of WTI is -0.02% with a range of 4.30%, while comparatively the standard averaged at -12.03% but with a range of 203.57%. Conversely for LME, the mean yield for hedging was close to zero while positive at 1.33% compared to the standard at -1.13%, yet the range for hedging is considerably smaller at 16.87% than 130.88% for the standard. HRC’s mean yield is 2.20% for hedging and the standard is -6.67%. Again the range remains smaller for hedging, 72.95%, compared to the standard at 116.39%.

Figure 4.16: Twelve months hedge and standard actions for WTI

53

Figure 4.17: Twelve months hedge and standard actions for LME

Figure 4.18: Twelve months hedge and standard actions for HRC 54

4.4.2.2 Eighteen Months The result of the analysis for 18M actions, hedge and standard, is depicted in Figures 4.19 – 4.21 for WTI, LME, and HRC respectively. For this coverage length, the hedged mean yield of WTI is -0.01% with a range of 5.17%, while comparatively the standard averaged at -16.91% but with a higher range of 226.33%. Conversely for LME, the mean yield for hedging is 1.19% compared to the standard at -7.35%. The LME range for hedging is 20.23% compared to 114.29% for the standard. HRC’s mean yield is 2.61% for hedging and the standard is -11.39%. Again the range remains smaller for hedging, 56.94%, compared to the standard at 125.91%.

Figure 4.19: Eighteen months hedge and standard actions for WTI

55

Figure 4.20: Eighteen months hedge and standard actions for LME

Figure 4.21: Eighteen months hedge and standard actions for HRC 56

4.4.2.4 Summary For the medium durations, the data show that the mean for hedging is close to 0 while the mean for standard deviates farther from zero and is negative. The range is always larger for the standard than the hedge. For side-by-side comparison of medium duration coverage lengths per derivative see Figure 4.22. Boxplot of WTI 12 M H, WTI 18 M H, LME 12 M H, LME 18 M H, ... 0.5

Data

0.0 -0.5 -1.0 -1.5

HEDGE

-2.0

T W

2 I1

M

H T W

8 I1

M

H E LM

12

M

H E LM

18

STANDARD M

H

C HR

12

M

H C HR

18

M

H

W

T

2 I1

M

S E LM

12

M

S

C HR

12

M

S

T W

8 I1

M

S E LM

18

M

S

C HR

18

M

S

Figure 4.22: Comparison of two actions for WTI, LME and HRC on medium duration

4.4.3 Long Duration The results for the actions taken over long durations are shown in Table 4.5. Coverage lengths of 2Y, 3Y, and 5Y are representative for long durations. Note: Data for LME and HRC is not available for the 5Y coverage length.

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Table 4.5: Analysis Results for Long Duration Actions

4.4.3.1 Two Years The result of the analysis for 2Y actions, hedge and standard, is depicted in Figures 4.23 – 4.25 for WTI, LME, and HRC respectively. For this coverage length, the hedged mean yield of WTI is 0% with a range of 5.25%, while comparatively the standard averaged at -21.74% but with a range of 219.78%. For LME, the mean yield for hedging is 0.52% compared to the standard at -12.51%. The range for hedging is 20.27%, which is less than the standard at 137.54% . HRC’s mean yield is 3.05% for hedging and the standard is -17.56%. Again the range remains smaller for hedging, 61.07%, compared to the standard at 129.35%.

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Figure 4.23: Two years hedge and standard actions for WTI

Figure 4.24: Two years hedge and standard actions for LME 59

Figure 4.25: Two years hedge and standard actions for HRC

4.4.3.2 Three Years The result of the analysis for 3Y actions, hedge and standard, is depicted in Figures 4.26 – 4.28 for WTI, LME, and HRC respectively. For this coverage length, the hedged mean yield of WTI is 0.03% with a range of 5.87%, while comparatively the standard averaged at -31.57% but with a higher range of 211.09%. For LME, the mean yield for hedging is 2.35% compared to the standard at -8.67%. The LME range for hedging is 14.54% compared to 117.11%. HRC’s mean yield is 6.02% for hedging and the standard is -18.26%. Again the range remains smaller for hedging, 52.78%, compared to the standard at 88.67%.

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Figure 4.26: Three years hedge and standard actions for WTI

Figure 4.27: Three years hedge and standard actions for LME 61

Figure 4.28: Three years hedge and standard actions for HRC

4.4.3.3 Five Years The result of the analysis for 5Y actions, hedge and standard, is depicted in Figure 4.29 for WTI. Data is not available for LME and HRC. For this coverage length, the hedged mean yield of WTI is 0.08% with a range of 5.36%, while comparatively the baseline averaged at -53.87% but with a higher range of 397.74%.

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Figure 4.29: Five years hedge and standard actions for WTI

4.4.3.4 Summary For the long durations, the data shows that the mean for hedging remains near zero while the mean for standard deviates farther, even more so as coverage length increases. The range is always smaller for the hedge than the standard. For side-by-side comparison of long short duration coverage lengths per derivative see Figure 4.30.

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Boxplot of WTI 2 Y H, WTI 3 Y H, WTI 5 Y H, LME 2 Y H, LME 3 Y H, ...

0

Data

-1

-2

-3

HEDGE

STANDARD

-4

W

2 TI

Y

H T W

I3

Y

H T W

I5

Y

H E LM

2

Y

H E LM

3

Y

H C HR

2

Y

H C HR

3

Y

H W

T

I2

Y

S W

T

I3

Y

S W

5 TI

Y

S E LM

2

Y

S E LM

3

Y

S C HR

2

Y

S C HR

3

Y

S

Figure 4.30: Comparison of hedge and standard actions for WTI, LME and HRC on long duration 4.5 STATISTICAL SIGNIFICANCE In order to determine if the statistical results are significant, certain tests must be performed. Of particular interest is the significance between the difference in means and in the variances of the two actions, standard and hedging. A two-sample t-test compares the means and determines if there is a significant difference between them or if it is attributed to random chance. For this test, the null hypothesis is that the mean of the standard action is equal to the mean of the hedge action with the alternate hypothesis being that they are not equal. The other test of particular interest is for equal variances, or F-test. The null hypothesis is that the variance of the two actions is equal and the alternative hypothesis is that they are not. For both tests, a common level of 95% was used. The p-value represents the probability of obtaining a test value at least as extreme as the one observed if the null hypothesis is true. So for statistical 64

analysis interpretation, when a p-value yielded less than 0.05, the null hypothesis could be rejected. The test reports for 1M coverage length for WTI are displayed in Table 4.6 and 4.7. The null hypothesis can be rejected for both equal means and variances. Thus, the mean is not only closer to zero for the hedge action but it is significantly different than the standard action. The variance is smaller for hedging and is also significant. Table 4.6: Two-Sample T-Test and CI: WTI 1 M H, WTI 1 M S Two-sample T for WTI 1 M H vs WTI 1 M S N Mean StDev SE Mean WTI 1 M H 326 -0.00005 0.00470 0.00026 WTI 1 M S 326 -0.0080 0.0868 0.0048 Difference = mu (WTI 1 M H) - mu (WTI 1 M S) Estimate for difference: 0.00794 95% CI for difference: (-0.00153, 0.01741) T-Test of difference = 0 (vs not =): T-Value = 1.65 P-Value = 0.100 DF = 326

Table 4.7: Test for Equal Variances: WTI 1 M H, WTI 1 M S 95% Bonferroni confidence intervals for standard deviations N Lower StDev Upper WTI 1 M H 326 0.0043159 0.0046963 0.0051471 WTI 1 M S 326 0.0797589 0.0867878 0.0951201 F-Test (Normal Distribution) Test statistic = 0.00, p-value = 0.000

The test reports for all the derivatives and the coverage lengths are in Appendix C (Minitab, 2007). All of the results of the necessary tests are tabulated in Table 4.8. The p-values are stated and the shading shows if the null hypothesis can be rejected at 95%. The null is always rejected for the difference of variances between the standard action and hedge action, 65

making the variances significantly different. However, the difference of the means is only significant for all coverage lengths of WTI except for the 1M coverage length and for HRC at the 18M coverage length and long durations.

Table 4.8: Parametric Test Results Reject H0 at 95%

Short Duration

1M

3M

Medium Duration

6M

12 M

18 M

Long Duration

2Y

3Y

5Y

WTI LME HRC WTI LME HRC WTI LME HRC WTI LME HRC WTI LME HRC WTI LME HRC WTI LME HRC WTI LME HRC

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Parametric F-test T-test 0 0.1 0 0.179 0.036 0.786 0 0.002 0 0.232 0 0.576 0 0 0 0.496 0 0.317 0 0 0 0.634 0 0.066 0 0 0 0.192 0 0.018 0 0 0 0.11 0 0.009 0 0 0 0.216 0.024 0.003 0 0 N/A

As further substantiation, if the assumptions for the previous tests are considered violated, namely normality, nonparametric tests were performed. First, normality must be determined. The Anderson-Darling test is a measure for normality as it tests if a sample of data is from a population with a specific distribution. Using Minitab (2007), the Anderson-Darling test was performed with the results of WTI 1M H and S displayed in the top right corner of Figures 4.4 and 4.5 (Other coverage lengths and derivative types are shown in Appendix B). For nonparametric data, medians are compared. An appropriate test is the Mann-Whitney test that calculates the difference between two population medians, in this case the standard and hedge actions. For this test, the null hypothesis is that the median of the standard action is equal to the median of the hedge action with the alternate hypothesis being that they are not equal. A nonparametric test for equal variances is Levene’s test that compares the variance of the hedge action to the standard action. The null hypothesis is that the variance of the two actions is equal and the alternative hypothesis is that they are not. The Mann-Whitney test results for WTI 1M are tabulated in Table 4.9, along with Levene’s test results in Table 4.10. For both tests, a common level of 95% was used. The p-value represents the probability of obtaining a test value at least as extreme as the one observed if the null hypothesis is true. So for statistical analysis interpretation, when a p-value yielded less than 0.05, the null hypothesis could be rejected. Similarly to what was portrayed previously, the reports are summarized for all coverage lengths and derivative types in Table 4.11 with shading to show significance at 95%.

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Table 4.9: Mann-Whitney Test and CI: WTI 1 M H, WTI 1 M S N Median WTI 1 M H 326 0.00000 WTI 1 M S 326 -0.01168 Point estimate for ETA1-ETA2 is 0.01155 95.0 Percent CI for ETA1-ETA2 is (0.00298,0.01739) W = 112797.0 Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at 0.0082 The test is significant at 0.0082 (adjusted for ties) Table 4.10: Test for Equal Variances: WTI 1 M H, WTI 1 M S 95% Bonferroni confidence intervals for standard deviations N Lower StDev Upper WTI 1 M H 326 0.0043159 0.0046963 0.0051471 WTI 1 M S 326 0.0797589 0.0867878 0.0951201 Levene's Test (Any Continuous Distribution) Test statistic = 346.61, p-value = 0.000 Table 4.11: Non-Parametric Test results Normality Reject H0 at 95% WTI

SHORT DURATION

1M

LME HRC WTI

3M

LME HRC WTI

6M LME

A-D H S H S H S H S H S H S H S H S

NonParametric MannLevene's Whitney

Sample Size

CLT

326

Yes

0

0.0082

56

Yes

0

0.6312

53

Yes

0.078

0.8422

324

Yes

0

0.0003

54

Yes

0

0.2569

51

Yes

0.001

0.9307

321

Yes

0

0.0022

51

Yes

0

0.4739

0.005 0.005 0.090 0.005 0.428 0.157 0.005 0.005 0.288 0.006 0.005 0.138 0.005 0.005 0.589 0.020

68

Normality Reject H0 at 95% HRC

MEDIUM DURATION

WTI 12 M

LME HRC WTI

18 M

LME HRC WTI

2Y

LME

LONG DURATION

HRC WTI 3Y

LME HRC WTI

5Y

A-D H S H S H S H S H S H S H S H S H S H S H S H S H S H S

NonParametric MannLevene's Whitney

Sample Size

CLT

48

Yes

0

0.6417

315

Yes

0

0

43

Yes

0

0.214

42

Yes

0

0.19

309

Yes

0

0

39

Yes

0

0.0674

36

Yes

0

0.1215

303

Yes

0

0

33

Yes

0

0.608

30

Yes

0

0.0191

291

Yes

0

0

21

No

0

0.8999

18

No

0.013

0.0042

267

Yes

0

0

0.005 0.263 0.005 0.005 0.713 0.009 0.005 0.252 0.005 0.005 0.378 0.005 0.072 0.066 0.005 0.005 0.297 0.005 0.005 0.09 0.005 0.005 0.669 0.019 0.118 0.597 0.005 0.005

LME N/A HRC

The null is rejected for the difference of variances between the standard action and hedge action for all instances except for HRC 1M. Again this is significant because the variances are 69

different. With slight changes, the difference of the medians is significant for all coverage lengths of WTI and HRC at long durations. For the normality test, the null hypothesis is that the distribution is normal and it is rejected 27 out of the 44 times. However, the central limit theorem provides the ability to use parametric tests with the possible exception of small sample size, those under 30. This is only violated for the 3Y coverage length of LME and HRC, yet only LME standard action rejects the null hypothesis of normality. 4.6 SUMMARY This analysis clearly demonstrates that hedging can be beneficial. The methodology looked at two commodities, oil and steel, through several specific derivatives and across various project durations. For a hedge strategy to be beneficial, the mean should be close to zero, or closer to zero and not negative, have a smaller range, and have less variability than the standard. This is proven for all ranges and the difference in variability is significant at 95 %. The means of the hedge are superior to the standard action for most instances but are significant for only WTI and certain HRC coverage lengths. The lack of every LME instance and for HRC in totality to meet the objective may be due to novelty, small volume, or other issues in comparison to WTI. Overall, the results show application of hedging will better meet a project’s budget success criteria and provide greater potential to reduce possible variation. This satisfies research objective two and justifies the need for development of a hedging strategy for construction projects, research objective three. Application of a hedge on a project is illustrated in Figure 4.31. After a project has been identified, and the materials budget determined, exposure can be calculated. Exposure is the difference between what has been estimated or budgeted and the final price that will be paid. Although the participant has tried to capture this in the budget, the projection could be 70

conservative and fall short of the actual price. If exposure is deemed at risk to market volatility and price escalation, this risk can be mitigated through a properly executed hedge that allows the success criteria to be reached.

Figure 4.31: Project risk influence diagram Having demonstrated that hedging can be beneficial, specific actions for utilizing derivatives need to be defined with pertinent details provided. This strategy has a broad impact and meaningful application to the E&C industry working in an increasingly competitive and volatile marketplace. A proactive strategy that employs derivatives trading, specifically for material pricing control, is proposed in Chapter 5.

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CHAPTER 5: Proposed Risk Mitigation Strategy for Construction Projects

5.1 INTRODUCTION This study investigates the use of financial hedges on price exposure in the E&C industry. It is well documented how other industries have been employing this strategy (Bartram et al., 2009; Geczy et el., 1997) and it has now been shown how this strategy is applicable to the E&C industry. Of particular importance to note, is the hedging capability on project level regardless of one specific sector, scale, delivery type, location, and participant. In identifying a strategy for materials price risk mitigation, the importance of market structures, available contracts, and types of hedges are all clearly stated. The markets have clearly defined structures and contract types as well as different commodity products. The hedges most likely to be executed were discussed in Chapter 3 and examples provided for swaps, futures, options, and collars. Financial instruments can mitigate materials price risk in construction projects through the reduction of exposure to raw material volatility. Proper contractual arrangement will help identify which participant bears the actual price risk and therefore should use the various financial instruments. To avoid the negative reputation of financial hedge funds, it is important to note that the goal of a proper hedge strategy for the E&C industry on construction projects is not to make profits but to protect against escalating losses. When implemented correctly the

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contractual commitment can be met. Further work in this area can help identify the better hedge strategy per project type, owner or contractor perspective, and commodity. The benefits of implementing a price risk mitigation plan around commodity markets are vast. One such benefit is that price risk exposure can be managed and controlled according to project and contract type. The exchange-traded markets allow for transparency in price, facilitating possible negotiations with other market influencers. Experience in other commodity markets has shown that there is a high correlation between spot prices of different related products. Therefore, in the case of steel products, this could mean that a futures contract for one product could serve as a proxy for other products, accounting for the possible basis risk. This would allow the E&C industry to hedge all the required project materials products in one forward contract or option. With the proper identification of the steel exchanges and its relative infancy, financial hedging of steel materials offers a great opportunity for the E&C industry to start to understand their function and adopt its use. It is also important to point out the challenges the E&C industry may face. First, the knowledge and understanding of financial instruments must be addressed, possibly through education or consultants. Additionally, the authority to execute or implement a hedge would need to be granted internal to the project or firm. Lastly, the E&C industry must accept and have confidence in the market structure. The divergence of the physical and futures prices is possible if speculation is not monitored correctly or there is too little liquidity. “Cornering the market,” a form of market manipulation, would lift the futures price and leave the spot market behind. However, with the established structures in the above mentioned markets, steps have been taken to prevent this from occurring, such as

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through physical delivery of products. Clearly established rules and constant monitoring ensures that the markets have proper oversight. Decision making regarding capital expenditure on construction projects can be more objective and financial expectations can be realized according to a properly executed strategy. The enhanced ability to accurately plan and for more certain cash flow should also reduce the fluctuations during project controls in general. The historical analysis, provided in Chapter 4, of the three commodities was performed with the intent of demonstrating the strategy applicability and impact of hedging regardless of coverage length and project duration. Statistical analysis confirmed that hedging is beneficial and significant, offering less variation and means close to zero. 5.2 MODEL Due to the unique nature of each construction project, various stages are necessary to mitigate materials price risks. A proactive strategy must fit within the risk tolerance framework of each participant, i.e. potential hedger, project and market. There is not a uniform action that is right for each level but particular per circumstance. The action items that need to be considered to execute the strategy are depicted in Figure 5.1 according to level and stage.

Figure 5.1: Model with key action items for materials price mitigation Every piece is vital for a successful hedging strategy that neutralizes materials price escalation. The individual stages, level and action items are discussed below in further detail. 74

5.2.1 Guidance Each such participant needs to have guidelines in place that provide the general framework for risk mitigation. First, a corporate philosophy should be crafted that clearly establishes the motivation for the use of financial instruments, whether to increase profit or to avoid losses. This is noteworthy for signaling to internal personnel whether you are trying to hedge materials price escalation or to be a speculator in the marketplace. By avoiding losses you will affect profit, but the primary objective of any such participant should be mitigation and not financial gains, as the latter may not be restricted to the project and could cause greater exposure to market risks. Once the objectives are defined and understood on a corporate level, personnel must be put in place to handle the strategy. A company’s current organizational structure may be accommodating or new roles could be required, yet all would need to assess the ability and education of its employees regarding finance and accounting of commodity markets and derivatives transactions. Internally selected personnel need to then be delegated the authority and responsibility to act in accordance with corporate philosophy and objectives as projects arise. 5.2.2 Identification Each project and its terms/conditions must be identified. The phase of the project lifecycle helps determine which participant bears the risk of material escalation. Each project has documentation, typically in the form of plans and specifications, showing required materials and the quantities as well as a schedule determining material deliverable dates and estimate of costs of materials. Once the construction participant acknowledges the risk they bear according to the project specific variables, total risk exposure for materials price escalation can be calculated.

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5.2.3 Assessment The next stage by the participant with the assumed exposure is to assess each material according to its raw good or underlying commodity. Various markets exist for each commodity and typical ones are listed in Table 5.1.

Table 5.1: Typical Commodity Markets for Construction Projects Exchange Abbreviation Location Product Types Agriculture, Ethanol, Treasuries, Chicago Mercantile Chicago, United Equity Index, Metals, Exchange (CME CME States Currencies, Eurodollars, Group) Weather Atlanta, Intercontinental Energy, Emissions, Agricultural, ICE Georgia, United Exchange Biofuels States Dubai Gold & DGCX Dubai Precious Metals Commodities Exchange Shanghai Futures Industrial metals, Gold, Fuel Oil, Shanghai, China Exchange Rubber Singapore Mercantile Metals, Agricultural, Energy, SMX Singapore Exchange Currencies, Commodity Indices Tokyo Commodity TOCOM Tokyo, Japan Energy, Metals, Agricultural Exchange Amsterdam, Climex CLIMEX Emissions Netherlands European Climate ECX Europe Emissions Exchange London Metal LME London, UK Metals, Plastics Exchange Risk Management Hannover, RMX Agricultural Exchange Deutschland European Energy Leipzig, EEX Energy, Emissions Exchange Deutschland

Each market is structured according to trading and physical specification, according to the commodity. Every commodity contract has standardized terms that are determined by the exchange, rather than by participants, as shown in Table 5.2. These standardized terms will 76

include the amount of the commodity to be delivered (contract size), delivery period, the last trading day, the delivery location or locations, and acceptable qualities or grades of the commodity.

Table 5.2: Example of Commodity Contract Terms U.S. Midwest Domestic Hot-Rolled Coil Steel Index Futures Product HRC, Clearing: HR Symbol Venue CME Globex, CME ClearPort Hours Sunday – Friday 6:00 p.m. – 5:15 p.m. with a 45CME Globex: (All Times minute break each day beginning at 5:15 p.m. are New CME Sunday – Friday 6:00 p.m. – 5:15 p.m. with a 45York ClearPort: minute break each day beginning at 5:15 p.m. Time/ET) Contract 20 short tons Size Price U.S. dollars and cents per ton Quotation Minimum $1.00 per short ton Fluctuation The floating price for each contract month is equal to the average Floating price calculated for all available price assessments published for that Price given month by the CRU U.S. Midwest Domestic Hot-Rolled Coil Steel Index. Termination Trading terminates on the business day prior to the last Wednesday of Trading of the named contract month. Listed Trading is conducted in 24 consecutive months. Contracts Settlement Financial Type Position NYMEX Position Limits Limits Rulebook 920 Chapter Exchange These contracts are listed with, and subject to, the rules and Rule regulations of NYMEX.

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A thorough examination of the markets is necessary to have confidence in its structure and an understanding of its products. The participant may also want to prepare a fundamental market forecast for each material and the commodity. Formulating charts can provide a confirmation of fundamental forecasts and help determine trends and patterns. 5.2.4 Determination Attributable to the unique nature of projects, hedging is not automatically applicable, requiring the participant to customize the strategy and make the final decision to hedge on a project-by-project basis. First, the participant will want to look at the exposure and meeting target objectives. Some of the possible questions for consideration are: Given current market conditions and price levels, do costs line up with the budgeted amount? What is the project’s status according to life cycle and material delivery? What is the degree to which the company is risk averse? Each participant will also want to determine whether to hedge all of the material exposure or only portions of it on a project. This will be greatly influenced according to the degree of risk aversion of a participant. Their aversion will further influence the type of financial derivative instrument to implement for hedging. Figure 5.2 depicts a decision tree of these choices.

78

Figure 5.2: Hedge decision tree Then the participant must determine when, what, and how much to hedge. Additionally, a participant needs to decide whether to hedge all at once or scaled in over time, using forecasts to heighten judgment. 5.2.5 Implementation Once the decision is made to hedge, the hedge must now be implemented. The hedge will be placed through an order to the predetermined clearing house. Once the order is executed, the participant must ensure that the position is handled internally according to proper accounting procedures and applicable margin. Further, the participant should oversee and monitor the order

79

so appropriate actions can be taken if the project changes or the open position is moving against its expectations.

5.2.6 Settlement Once the hedge is implemented and performs, the participant can close its open position through settlement. The financial derivative will be settled in the clearing house before actual delivery of the physical good. This allows the hedge to be a solely financial transaction and the material procurement a physical one. Additionally, a participant has the flexibility of settling the hedge at any time, particularly if it is performing better than estimated or if project circumstances have changed. The participant may even realize extra return by getting out of the hedge earlier than planned. 5.2.7 Evaluation The strategy and the referenced actions enable a participant to use hindsight and evaluate the performance of the hedge. Were the main project objectives met? If yes, the company may gain confidence and continue to use hedges. If not, why not? Do assessment measures need to be altered, and do the deterministic criteria need to be adjusted? These are pertinent questions for performance evaluation and improvement. The preceding sections carefully laid out the foundation necessary for a workable strategy.

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CHAPTER 6: Industry Application and Validation

6.1 INTRODUCTION The proposed model in Chapter 5 satisfies the third objective and provides the framework to approach objective four. To verify the positive, mitigating impact of the proposed model, an appropriate means for validation is offered. According to Rescher (1977), human knowledge consists of two distinct forms, theses and methods. Theses are defines statements or assertions about the world and methods are defined ways of doing things. He claimed that theses were the emphasis of scientific research and methodological knowledge required a novel approach for validation. His reasoning was that methods have pragmatic value, either effective or ineffective, and are not true or false. Therefore, the validity of a method can only be established by applicative success in practice and adopted as such. Sadiq et al. (2004) addressed the issues of workflow modeling with particular importance given to process validation. Validation was defined as, “a means by which the process design can be checked against expected execution behavior…”. A study of verification and validation of models by Stewart (1997) stated that “a model is only validated with respect to its purpose.” He further described various methods with the pertinent one to this research study being conceptual model validation. This method seeks to determine if the proposed model has sufficient scope and detail for its intended use, as well as having correct underlying assumptions. If so, the question of whether the model meets the objectives of the simulation study can be answered. 81

Through the execution of the proposed model using real-world industry conditions, feedback, in both quantitative and qualitative form, is obtained. Specific qualitative information, providing the basis for decisions is demonstrated by performing the stages of the model. The statistical findings and conclusion from Chapter 4 are quantitatively validated through simulation in stages: determination, implementation, settlement, and evaluation. This dual approach shows how the model’s framework facilitates a particular sector of the E&C industry to mitigate their materials price risk.

6.2 INDUSTRY DESCRIPTION An industry that regularly procures steel for projects is electric utilities, specifically for electric power transmission. The United States electric grid is comprised of more than 200,000 miles of high-voltage, 230 kilovolts and greater, transmission lines (EEI, 2012). These lines are the backbone to a reliable system that originated more than 100 years ago. With a growing population and evolving technological affluence, the demand for electricity is ever increasing. This puts stress on the existing grid, thus requiring upgrades and expansion to meet the newfound, growing needs. In order to meet this build out, utilities are earmarking billions of dollars for investment over the next decade. Edison Electric Institute (2012) stated that “shareholder-owned electric companies invested more than $53 billion in the nation's transmission system from 2005 to 2010. In fact, for the first time, transmission expenditures exceeded $10 billion in 2010. The industry is expected to spend an additional $54 billion from 2011 to 2014.” Thus the amount of exposure to materials price risk is substantial and demands new mitigation techniques be applied. The proposed mitigation technique will be used on a portfolio of electric transmission projects as discussed below. 82

6.3 GUIDELINES The transmission owner has formal procedures in place and has determined key personnel. Every project manager is designated to work with market specific brokers for the placement of a hedge. The philosophy of the owner is to avoid losses through the escalation of material costs. 6.4 IDENTIFICATION The projects have owner assumed materials risk with predetermined budgets and dates as represented in Table 6.1 and completely in Appendix D. Table 6.1: Representative Transmission Line Projects

Jan-08

InService Jun-12

Apr-08

Jul-12

Oct-08 Oct-08 Oct-08 Oct-08 Oct-08 Oct-08 Oct-08 Oct-08 Oct-08 Oct-08 Oct-08 Oct-08 Nov-08

Dec-09 Sep-09 Oct-10 Apr-10 Feb-12 May-10 Mar-10 Dec-08 Jul-10 Sep-10 Apr-11 Oct-09 Jun-09

Filing

Project David Jct. - Bingham 138kV Hybrid Energy Center-Clinch River 138kV Transmission Line Arnold-Vinton-Dysart-Washburn 161kV Reconductor G503 Noble Wind Farm Grand Rapids SAG limits Gray Road Grove Lake - Glenwood line rebuild Hubbardston Road Indiana Arsenal Jct to CMC new 138kV line Keystone - Clearwater - Stover 138 kV line Phase 1 MacSteel GOAB Marathon/Navarre Martinsville to Martinsville SE 69kV Jct Uprate Speed to LGEE Trimble 345kV tie Milroy-Sheridan 69kV line addition

Budget (Millions) 11.70 50.00 14.10 7.83 1.00 4.14 16.35 0.17 5.06 17.00 0.20 0.64 0.01 3.20 0.63

These projects were sourced from the PJM Interconnection (PJM) and Midwest Independent Transmission System Operator (MISO). MISO and PJM are two entities that control electric transmission systems for owner utilities and coordinate all transmission projects 83

in its region. For the purpose of this study, the related regulatory filing will be used as the project initiation date and in-service for materials procurement date, thereby formulating the hedge duration.

6.5 ASSESSMENT Steel can be considered the most important industrial raw material in construction industries. Global production of steel is approximately 20 times higher than that of all nonferrous metals combined. China, Japan, and the United States are the leading producers and consumers of steel in the world. Demand for steel has accelerated globally due to demand from China, India and other developing nations and the share in which these countries partake will increase significantly in the future. Steel products are broadly divided into flat, a hot or cold plate product with varying dimensions of 100mm to 200mm as well as 1mm to 10mm, and long, bars or rods of varying sizes. Steel derivatives are relatively new, with China attempting to establish a market as early as the 1990s before abandoning the market entirely. Then in the early 2000s, Koch Metals Trading Ltd. and Multi Commodity Exchange have offered steel derivatives in an OTC market. More recently, even with the active OTC markets, exchange-traded markets on three major global commodity exchanges have emerged for steel contracts. These three exchanges are the CME Group (CME, 2011), Dubai Gold and Commodities Exchange (DGCX, 2011), and London Metal Exchange (LMEX, 2011). Each exchange was originally designed to serve a slightly different section of the steel supply chain and is structured differently as a result. CME targeted US hot-rolled coil based steel product for 20 short-ton lots forward for 24 months. The price used to settle the contract is based on an index compiled by surveying opinions held by a cross84

section of the steel industry in conjunction with 20 other product and regional price indices. DGCX offers 10 ton deliverable lots of reinforcement bar per contract monthly up to 4 months forward. The LMEX launched trading of two physically deliverable billet contracts, Mediterranean and Far East. Both contracts are 65 ton lots with spot, 3 month, and 15 month forward. The variety of contracts offered in the above mentioned markets should allow the construction participant to properly match each project’s steel requirements as close as possible. 6.6 DETERMINATION The amount of exposure for the owner is project specific and based on the tonnage of steel required. For this study, the budgeted cost for steel is based on the market price at the time of filing and thus considered in alignment. The steel costs are derived from the assumption that the project cost is for a typical transmission line, and American Electric Power Company (2012) cost breakdown for a transmission line is that materials account for 41% of which 60% is structures. The derivative that will be used is HRC due to its close approximation to structural steel costs. (Eq. 6.1) Tonnage was calculated as the steel cost divided by the spot price of HRC on the filing date. (Eq. 6.2) The owner is risk averse due to impact of multiple concurrent projects and will use a full hedge strategy. 6.7 IMPLEMENTATION The Project Manager contacts the CME broker to handle the hedge for each project. He then records and monitors each order per project. Five projects were randomly selected from the

85

transmission projects listed in Table 6.1.

The data set for HRC from Chapter 4 is appropriate

for use again. The information for the randomly selected projects is tabulated in Table 6.2. The projects range in cost from $45 million to $40,000 with steel cost exposures between $11 million and $8,610.

Long

Medium

Short

Table 6.2: Randomly Selected Projects

Filing

InService

Apr-12 Jan-10 Nov-12

Sep-12 Feb-10 Jan-13

Jan-10 Dec-11 Sep-09 Sep-10 Sep-11 Mar-09 Aug-09 Aug-09 Aug-09 Oct-08 Dec-08 Apr-10

Project Name

HE Salisbury - Georgetown 69kV rebuild Sigel-Auburndale-Rozellville Line Madison-Tanners Creek 138kV Rebuild Chaffee Creek-Plainfield 69 kV Jun-10 line Jun-12 Lore-Seippel 69 kV Rebuild Dec-10 Goshen Jct. Cir 6976 - Recond 2.1 Miles Jun-12 Eau Claire - Madison Street Rebuild Dec-12 Keystone – Sorenson Jun-10 New Oak Ridge-Verona 138-kV line Jun-11 Loretto - Piney Grove May-12 Sporn Dec-11 Lackawanna Feb-12 Grove Lake - Glenwood line rebuild Dec-11 Wood Pole Replacement 2011 Brokaw-State Farm Line 1596 – May-12 Reconductor

Project Cost (million $) 1.67 2.08 0.41

Steel Cost $409,590 $511,927 $100,860

4.96

$1,219,371

0.94 1.20 0.58 0.04 3.40 27.30 27.77 23.54 7.83 4.06

$231,240 $295,200 $142,680 $8,610 $836,400 $6,715,800 $6,832,158 $5,792,026 $1,925,992 $998,760

45.00

$11,070,000

6.8 SETTLEMENT The settlement of the hedges implemented was determined using historical spot and closing prices of futures contracts pertaining to HRC, and the effective price calculation as defined in Chapter 4. The results of the settlement are stated in Table 6.3. The hedge strategy settles for less than the budget amount three times out of the 15 projects. The range spanned projects that were $352,400 under and $901,700 over the budget. While these figures are not 86

optimum in meeting the success criteria, the hedge needs to be compared to the standard action for further evaluation.

Long

Medium

Short

Table 6.3: Results of Settlement Filing

InService

Apr-12

Sep-12

Jan-10

Feb-10

Nov-12

Jan-13

Jan-10

Jun-10

Dec-11

Jun-12

Sep-09

Dec-10

Sep-10

Jun-12

Sep-11

Dec-12

Mar-09

Jun-10

Aug-09 Aug-09 Aug-09

Jun-11 May-12 Dec-11

Oct-08

Feb-12

Dec-08

Dec-11

Apr-10

May-12

Project Name Steel Cost Hedge Difference HE Salisbury - Georgetown $409,590 $425,597 -$16,007 69kV rebuild Sigel-Auburndale$511,927 $513,987 -$2,060 Rozellville Line Madison-Tanners Creek $100,860 $101,260 -$400 138kV Rebuild Chaffee Creek$1,219,371 $1,376,175 -$156,804 Plainfield 69 kV line $231,240 $235,996 -$4,756 Lore-Seippel 69 kV Rebuild Goshen Jct. Cir 6976 $295,200 $290,616 $4,584 Recond 2.1 Miles Eau Claire - Madison Street $142,680 $146,255 -$3,575 Rebuild $8,610 $8,353 $257 Keystone - Sorenson New Oak Ridge-Verona $836,400 $897,191 -$60,791 138-kV line $6,715,800 $7,617,505 -$901,705 Loretto - Piney Grove $6,832,158 $7,313,444 -$481,286 Sporn $5,792,026 $6,051,792 -$259,767 Lackawanna Grove Lake - Glenwood $1,925,992 $2,500,710 -$574,718 line rebuild Wood Pole Replacement $998,760 $646,360 $352,400 2011 Brokaw-State Farm Line $11,070,000 $11,324,018 -$254,018 1596 - Reconductor

6.9 EVALUATION Although the hedge was greater than the budget for 12 of the fifteen projects and the standard action was only nine times greater, the magnitudes were different. For the project that had a hedge cost of $901,700 over the budget, the standard was $4,071,494 over. The budget, standard, and hedge values are listed in Table 6.4 for comparison. The average costs for hedging 87

were always less than the standard action regardless of duration. This further means that hedging performed closer to the budgeted costs even though it was still more than the budgeted amount per duration. In total, hedging overran the budget by an average of $157,243 while the standard approach would have seen an escalation of $688,961 for steel cost. Consequently, the owner is satisfied with the choice of hedging. Table 6.4: Comparison of Results Budget $100,860 $1,219,371 $494,598

MIN MAX Short AVG DIFF MIN $8,610 MAX $6,715,800 Medium AVG $1,599,738 DIFF MIN $998,760 MAX $11,070,000 Long AVG $5,323,787 DIFF MIN $8,610 MAX $11,070,000 Total AVG $2,472,708 DIFF

Standard Hedge $100,170 $101,260 $1,529,385 $1,376,175 $551,956 $530,603 -$57,358 -$36,006 $7,680 $8,353 $10,787,294 $7,617,505 $2,490,038 $1,791,984 -$890,300 -$192,246 $845,295 $646,360 $10,679,024 $11,324,018 $6,443,011 $5,567,265 -$1,119,224 -$243,478 $7,680 $8,353 $10,787,294 $11,324,018 $3,161,668 $2,629,951 -$688,961 -$157,243

6.10 SUMMARY For a majority of the randomly selected projects, hedging mitigated the risk of escalating costs compared to the standard regardless of duration. Of particular importance, hedging had a considerably smaller variability and magnitude. By employing the proposed model, the electric transmission industry benefited and was closer to achieving project financial success criteria. 88

The model is not limited to any one particular industry, or only steel as a material, or the owner as the participant, or total risk aversion, and therefore could be employed in different ways according to project specific needs.

89

CHAPTER 7: Conclusions/Recommendations

7.1 CONCLUSION/ RECOMMENDATIONS The scope of this paper was to discover and develop materials price mitigation strategy for use in the E&C industry. First, existing strategies currently employed by the E&C industry were investigated and shown to be inadequate. Next, mitigation techniques used by other industries were found and their applicability and adaptability for construction projects were analyzed. In identifying a strategy for price risk management of materials, the importance of market structures, available contracts, and types of hedges were identified and clearly explained. The findings were that financial tools used by other industries are not only applicable in the construction industry, but unique in project specific application. In order to use this newfound technique of hedging on a construction project, a model was developed providing the key actions necessary to use financial derivatives for mitigating materials price risk. Particularly, this research examined the use of financial hedges on steel price exposure in the electric transmission industry. The proposed model mitigates steel risk, providing a significant and immediate benefit to the E&C industry. The benefit of this research is a proposed model that allows project participants to properly mitigate steel price risk. The impact is immense in its ability to tailor the model to any construction project, and its required materials. Furthermore, through the identification of existing financial instruments and its application, a better informed and more financially savvy construction industry will emerge. 90

7.2 LIMITATIONS Possible limitations are the lack of transparency in certain market structures and data availability. However, this was overcome through some of the stated alternatives and acknowledging that they simply exist. The scope of this research included the benefit analysis of risk mitigation techniques and omitted the cost impacts. Typical factors influencing costs span from the training of internal personal to utilize the strategy, to the salary and recruitment of an expert internal to the company, as well as the transaction costs. The transaction costs of hedging are often hard to capture without actually engaging in the market. These can include broker fees, margins, and taxes. For the purpose of this study they were deemed negligible, yet McDonald (2006) argues that the presence of transaction costs are reasons a company should not hedge. He provides the following examples for support: the expense of high transaction costs, the assessment requires expertise, the necessary monitoring and control, tax and accounting consequences, and complicated reporting. These were all considered and mentioned in the development of the proposed model, and may be a limiting factor on which participants could or would adopt this strategy. A common issue in using financial instruments is “basis risk”. Basis risk describes the risk that the value of the commodity being hedged may not change in tandem with the value of the derivative contract used to hedge the price risk. While the derivative may be highly correlated, significant basis risk can emerge if the relationship between the commodities breaks down. In an ideal hedge, the hedge would match the underlying position in every aspect, eliminating any chance of basis risk. In actuality, even if the derivatives contract is for the exact hedged commodity, basis risk remains a concern. Basis also represents the differential between a given commodity’s cash price and its nearest futures contract price. 91

The following three basis risks occur frequently in hedging: product-basis risk, time-basis risk, and locational-basis risk. Product-basis risk occurs when there is a mismatch in the quality, consistency, weight, or underlying product. For example, airlines frequently use crude oil contracts to hedge jet fuel, but obviously crude oil and jet fuel are two different commodities and hence have large product basis risk. Even within the same commodity category, such as crude oil, product-basis risk occurs because there are many types of crude oil varying in viscosity (such as heavy versus light crude) and sulfur content (sweet versus sour crude). Time-basis risk occurs when there is a mismatch in the time of the hedge. For example, if a hedger wishes to hedge long-term but only has short dated contracts available, time-basis risk is significant. Locationalbasis risk occurs when there is a mismatch in the price of the product from one location to another or a mismatch in the delivery point for the derivatives contract.

7.3 FUTURE WORK Future work in this area is vast. With the steel market still in its infancy, the growth potential is great with new derivative offerings on the horizon. Other financial instruments and commodity markets should be analyzed for applicable benefit to the construction industry. Once uncovered, not only the historical prices of each could be evaluated but their future prices could be forecast as well. The identification of one or more preferred hedge strategies for the E&C industry could also be determined and further promoted. Additionally, accounting, cash flow, and risk sharing could be investigated on a company and project level. The need for new procurement strategies and sourcing abilities may also arise from the use of the proposed model. Ultimately, best practices for installing organizational structure and processes for executing the model could be measured. 92

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APPENDIX A: WTI Price Data

Date Jan-1986 Feb-1986 Mar-1986 Apr-1986 May-1986 Jun-1986 Jul-1986 Aug-1986 Sep-1986 Oct-1986 Nov-1986 Dec-1986 Jan-1987 Feb-1987 Mar-1987 Apr-1987 May-1987 Jun-1987 Jul-1987 Aug-1987 Sep-1987 Oct-1987 Nov-1987 Dec-1987 Jan-1988 Feb-1988 Mar-1988 Apr-1988 May-1988 Jun-1988 Jul-1988

WTI Spot Futures 22.93 22.98 15.46 15.46 12.61 12.62 12.84 12.75 15.38 15.26 13.43 13.38 11.59 11.58 15.1 15.11 14.87 14.94 14.9 14.89 15.22 15.23 16.11 16.09 18.65 18.67 17.75 17.73 18.3 18.28 18.68 18.6 19.44 19.33 20.07 19.99 21.34 21.33 20.31 20.23 19.53 19.52 19.86 19.86 18.85 18.85 17.28 17.26 17.13 17.15 16.8 16.76 16.2 16.21 17.86 17.88 17.42 17.45 16.53 16.56 15.5 15.51

Aug-1988 Sep-1988 Oct-1988 Nov-1988 Dec-1988 Jan-1989 Feb-1989 Mar-1989 Apr-1989 May-1989 Jun-1989 Jul-1989 Aug-1989 Sep-1989 Oct-1989 Nov-1989 Dec-1989 Jan-1990 Feb-1990 Mar-1990 Apr-1990 May-1990 Jun-1990 Jul-1990 Aug-1990 Sep-1990 Oct-1990 Nov-1990 Dec-1990 Jan-1991 Feb-1991 Mar-1991 Apr-1991

15.52 14.54 13.77 14.14 16.38 18.02 17.94 19.48 21.07 20.12 20.05 19.78 18.58 19.59 20.1 19.86 21.1 22.86 22.11 20.39 18.43 18.2 16.7 18.45 27.31 33.51 36.04 32.33 27.28 25.23 20.48 19.9 20.83 98

15.53 14.46 13.8 13.98 16.28 17.98 17.82 19.44 20.94 20.03 19.99 19.66 18.55 19.59 20.1 19.83 21.09 22.64 22.11 20.41 18.58 18.46 16.86 18.64 27.18 33.69 35.92 32.3 27.16 24.7 20.54 19.88 20.82

May-1991 Jun-1991 Jul-1991 Aug-1991 Sep-1991 Oct-1991 Nov-1991 Dec-1991 Jan-1992 Feb-1992 Mar-1992 Apr-1992 May-1992 Jun-1992 Jul-1992 Aug-1992 Sep-1992 Oct-1992 Nov-1992 Dec-1992 Jan-1993 Feb-1993 Mar-1993 Apr-1993 May-1993 Jun-1993 Jul-1993 Aug-1993 Sep-1993 Oct-1993 Nov-1993 Dec-1993 Jan-1994

21.23 20.19 21.4 21.69 21.89 23.23 22.46 19.5 18.79 19.01 18.92 20.23 20.98 22.39 21.78 21.34 21.88 21.69 20.34 19.41 19.03 20.09 20.32 20.25 19.95 19.09 17.89 18.01 17.5 18.15 16.61 14.52 15.03

21.25 20.2 21.43 21.68 21.86 23.23 22.43 19.53 18.82 19.01 18.95 20.26 21 22.36 21.74 21.29 21.92 21.71 20.36 19.41 19.07 20.08 20.35 20.33 19.98 19.13 17.9 18.01 17.52 18.17 16.74 14.53 15.02

Feb-1994 Mar-1994 Apr-1994 May-1994 Jun-1994 Jul-1994 Aug-1994 Sep-1994 Oct-1994 Nov-1994 Dec-1994 Jan-1995 Feb-1995 Mar-1995 Apr-1995 May-1995 Jun-1995 Jul-1995 Aug-1995 Sep-1995 Oct-1995 Nov-1995 Dec-1995 Jan-1996 Feb-1996 Mar-1996 Apr-1996 May-1996 Jun-1996 Jul-1996 Aug-1996 Sep-1996 Oct-1996 Nov-1996 Dec-1996 Jan-1997 Feb-1997 Mar-1997 Apr-1997 May-1997

14.78 14.68 16.42 17.89 19.06 19.66 18.38 17.45 17.72 18.07 17.16 18.04 18.57 18.54 19.9 19.74 18.45 17.33 18.02 18.23 17.43 17.99 19.03 18.86 19.09 21.33 23.5 21.17 20.42 21.3 21.9 23.97 24.88 23.71 25.23 25.13 22.18 20.97 19.7 20.82

14.78 14.65 16.33 17.83 19.07 19.66 18.38 17.47 17.71 18.1 17.16 17.99 18.53 18.55 19.89 19.74 18.4 17.26 17.81 18.21 17.4 18 19.04 18.7 18.78 21.18 23.3 21.09 20.43 21.25 21.91 23.93 24.9 23.55 25.12 25.18 22.17 20.97 19.73 20.87

Jun-1997 Jul-1997 Aug-1997 Sep-1997 Oct-1997 Nov-1997 Dec-1997 Jan-1998 Feb-1998 Mar-1998 Apr-1998 May-1998 Jun-1998 Jul-1998 Aug-1998 Sep-1998 Oct-1998 Nov-1998 Dec-1998 Jan-1999 Feb-1999 Mar-1999 Apr-1999 May-1999 Jun-1999 Jul-1999 Aug-1999 Sep-1999 Oct-1999 Nov-1999 Dec-1999 Jan-2000 Feb-2000 Mar-2000 Apr-2000 May-2000 Jun-2000 Jul-2000 Aug-2000 Sep-2000

19.26 19.66 19.95 19.8 21.33 20.19 18.33 16.72 16.06 15.12 15.35 14.91 13.72 14.17 13.47 15.03 14.46 13 11.35 12.52 12.01 14.68 17.31 17.72 17.92 20.1 21.28 23.8 22.69 25 26.1 27.26 29.37 29.84 25.72 28.79 31.82 29.7 31.26 33.88 99

19.22 19.66 19.95 19.78 21.28 20.22 18.32 16.73 16.08 15.04 15.46 14.93 13.67 14.09 13.38 14.97 14.42 13.04 11.31 12.49 12.02 14.68 17.3 17.77 17.92 20.1 21.28 23.79 22.67 24.77 26.09 27.01 29.3 29.89 25.54 28.81 31.53 29.72 31.14 33.87

Oct-2000 Nov-2000 Dec-2000 Jan-2001 Feb-2001 Mar-2001 Apr-2001 May-2001 Jun-2001 Jul-2001 Aug-2001 Sep-2001 Oct-2001 Nov-2001 Dec-2001 Jan-2002 Feb-2002 Mar-2002 Apr-2002 May-2002 Jun-2002 Jul-2002 Aug-2002 Sep-2002 Oct-2002 Nov-2002 Dec-2002 Jan-2003 Feb-2003 Mar-2003 Apr-2003 May-2003 Jun-2003 Jul-2003 Aug-2003 Sep-2003 Oct-2003 Nov-2003 Dec-2003 Jan-2004

33.11 34.42 28.44 29.59 29.61 27.25 27.49 28.63 27.6 26.43 27.37 26.2 22.17 19.64 19.39 19.72 20.72 24.53 26.18 27.04 25.52 26.97 28.39 29.66 28.84 26.35 29.46 32.95 35.83 33.51 28.17 28.11 30.66 30.76 31.57 28.31 30.34 31.11 32.13 34.31

32.93 34.26 28.4 29.26 29.64 27.27 27.62 28.68 27.59 26.47 27.31 25.69 22.21 19.67 19.4 19.73 20.76 24.44 26.26 26.95 25.55 26.94 28.2 29.67 28.86 26.19 29.39 32.7 35.73 33.16 28.14 28.07 30.52 30.7 31.6 28.31 30.35 31.06 32.14 34.22

Feb-2004 Mar-2004 Apr-2004 May-2004 Jun-2004 Jul-2004 Aug-2004 Sep-2004 Oct-2004 Nov-2004 Dec-2004 Jan-2005 Feb-2005 Mar-2005 Apr-2005 May-2005 Jun-2005 Jul-2005 Aug-2005 Sep-2005 Oct-2005 Nov-2005 Dec-2005 Jan-2006 Feb-2006 Mar-2006 Apr-2006 May-2006 Jun-2006 Jul-2006 Aug-2006 Sep-2006 Oct-2006 Nov-2006 Dec-2006 Jan-2007 Feb-2007

34.69 36.74 36.75 40.28 38.03 40.78 44.9 45.94 53.28 48.47 43.15 46.84 48.15 54.19 52.98 49.83 56.35 59 64.99 65.59 62.26 58.32 59.41 65.49 61.63 62.69 69.44 70.84 70.95 74.41 73.04 63.8 58.89 59.08 61.96 54.51 59.28

34.5 36.72 36.62 40.28 38.05 40.81 44.88 45.94 53.09 48.48 43.26 46.85 48.05 54.63 53.22 49.87 56.42 59.03 64.99 65.55 62.27 58.34 59.45 65.54 61.93 62.97 70.16 70.96 70.97 74.46 73.08 63.9 59.14 59.4 62.09 54.35 59.39

Mar-2007 Apr-2007 May-2007 Jun-2007 Jul-2007 Aug-2007 Sep-2007 Oct-2007 Nov-2007 Dec-2007 Jan-2008 Feb-2008 Mar-2008 Apr-2008 May-2008 Jun-2008 Jul-2008 Aug-2008 Sep-2008 Oct-2008 Nov-2008 Dec-2008 Jan-2009 Feb-2009 Mar-2009 Apr-2009 May-2009 Jun-2009 Jul-2009 Aug-2009 Sep-2009 Oct-2009 Nov-2009 Dec-2009 Jan-2010 Feb-2010 Mar-2010

60.44 63.98 63.46 67.49 74.12 72.36 79.92 85.8 94.77 91.69 92.97 95.39 105.45 112.58 125.4 133.88 133.37 116.67 104.11 76.61 57.31 41.12 41.71 39.09 47.94 49.65 59.03 69.64 64.15 71.05 69.41 75.72 77.99 74.47 78.33 76.39 81.2

100

60.74 64.04 63.53 67.53 74.15 72.36 79.63 85.66 94.63 91.74 92.93 95.35 105.42 112.46 125.46 134.02 133.48 116.69 103.76 76.72 57.44 42.04 41.92 39.26 48.06 49.95 59.21 69.7 64.29 71.14 69.47 75.82 78.15 74.6 78.4 76.45 81.29

Apr-2010 May-2010 Jun-2010 Jul-2010 Aug-2010 Sep-2010 Oct-2010 Nov-2010 Dec-2010 Jan-2011 Feb-2011 Mar-2011 Apr-2011 May-2011 Jun-2011 Jul-2011 Aug-2011 Sep-2011 Oct-2011 Nov-2011 Dec-2011 Jan-2012 Feb-2012 Mar-2012 Apr-2012 May-2012 Jun-2012 Jul-2012 Aug-2012 Sep-2012 Oct-2012 Nov-2012 Dec-2012 Jan-2013 Feb-2013 Mar-2013

84.29 73.74 75.34 76.32 76.6 75.24 81.89 84.25 89.15 89.17 88.58 102.86 109.53 100.9 96.26 97.3 86.33 85.52 86.32 97.16 98.56 100.27 102.2 106.16 103.32 94.66 82.3 87.9 94.13 94.51 89.49 86.53 87.86 94.76 95.31 91.21

84.58 74.12 75.41 76.38 76.67 75.55 81.95 84.32 89.23 89.58 89.74 102.98 110.04 101.36 96.29 97.34 86.34 85.61 86.43 97.16 98.58 100.32 102.26 106.21 103.35 94.72 82.41 87.93 94.16 94.56 89.57 86.73 88.25 94.83 95.32 91.27

APPENDIX B: Summary of Simulated Actions

Summary for WTI 1 M H A nderson-Darling N ormality Test

-0.018

-0.012

-0.006

-0.000

0.006

0.012

0.018

0.024

A -S quared P -V alue <

7.29 0.005

M ean S tDev V ariance S kew ness Kurtosis N

-0.000053 0.004696 0.000022 0.21194 4.50491 326

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

-0.017344 -0.002108 0.000000 0.001994 0.023385

95% C onfidence Interv al for M ean -0.000565

0.000458

95% C onfidence Interv al for M edian -0.000195 9 5 % C onfidence Inter vals

0.004361

Mean Median -0.00050

-0.00025

0.00000

0.000239

95% C onfidence Interv al for S tDev

0.00025

101

0.00050

0.005087

Summary for WTI 1 M S A nderson-Darling N ormality Test

-0.45

-0.30

-0.15

0.00

0.15

0.30

A -S quared P -V alue <

1.56 0.005

M ean S tDev V ariance S kew ness Kurtosis N

-0.007997 0.086788 0.007532 -0.23310 3.51939 326

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

-0.480217 -0.058804 -0.011681 0.045789 0.325774

95% C onfidence Interv al for M ean -0.017453

0.001459

95% C onfidence Interv al for M edian -0.018781

-0.001182

95% C onfidence Interv al for S tDev

9 5 % C onfidence Inter vals

0.080598

0.094015

Mean Median -0.020

-0.015

-0.010

-0.005

0.000

Summary for WTI 3 M H A nderson-Darling N ormality Test

-0.018

-0.012

-0.006

-0.000

0.006

0.012

0.018

A -S quared P -V alue <

6.30 0.005

M ean S tDev V ariance S kew ness Kurtosis N

-0.000087 0.005160 0.000027 -0.20969 3.62722 324

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

-0.021429 -0.002288 0.000000 0.002293 0.020610

95% C onfidence Interv al for M ean -0.000651

0.000477

95% C onfidence Interv al for M edian -0.000317 9 5 % C onfidence Inter vals

0.004791

Mean Median -0.00075

-0.00050

-0.00025

0.00000

0.000566

95% C onfidence Interv al for S tDev

0.00025

102

0.00050

0.005592

Summary for WTI 3 M S A nderson-Darling N ormality Test

-0.9

-0.6

-0.3

0.0

0.3

0.6

A -S quared P -V alue <

2.81 0.005

M ean S tDev V ariance S kew ness Kurtosis N

-0.03078 0.17275 0.02984 -0.78839 5.79587 324

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

-1.00659 -0.12486 -0.03074 0.07434 0.60503

95% C onfidence Interv al for M ean -0.04966

-0.01190

95% C onfidence Interv al for M edian -0.05146

-0.01190

95% C onfidence Interv al for S tDev

9 5 % C onfidence Inter vals

0.16039

0.18719

Mean Median -0.05

-0.04

-0.03

-0.02

-0.01

Summary for WTI 6 M H A nderson-Darling N ormality Test

-0.04

-0.03

-0.02

-0.01

0.00

0.01

0.02

A -S quared P -V alue <

7.43 0.005

M ean S tDev V ariance S kew ness Kurtosis N

-0.000172 0.005841 0.000034 -0.98825 7.52781 321

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

-0.039024 -0.002566 0.000131 0.002515 0.022196

95% C onfidence Interv al for M ean -0.000813

0.000470

95% C onfidence Interv al for M edian -0.000293 9 5 % C onfidence Inter vals

0.005421

Mean Median -0.00100

-0.00075

-0.00050

-0.00025

0.00000

0.000508

95% C onfidence Interv al for S tDev

0.00025

103

0.00050

0.006332

Summary for WTI 6 M S A nderson-Darling N ormality Test

-0.9

-0.6

-0.3

0.0

0.3

0.6

A -S quared P -V alue <

1.51 0.005

M ean S tDev V ariance S kew ness Kurtosis N

-0.062919 0.239515 0.057367 -0.29627 1.30718 321

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

-0.955507 -0.196248 -0.059378 0.100113 0.692859

95% C onfidence Interv al for M ean -0.089221

-0.036618

95% C onfidence Interv al for M edian -0.079480

-0.012083

95% C onfidence Interv al for S tDev

9 5 % C onfidence Inter vals

0.222309

0.259630

Mean Median -0.10

-0.08

-0.06

-0.04

-0.02

Summary for WTI 12 M H A nderson-Darling N ormality Test

-0.018

-0.012

-0.006

0.000

0.006

0.012

0.018

A -S quared P -V alue <

4.74 0.005

M ean S tDev V ariance S kew ness Kurtosis N

-0.000180 0.005737 0.000033 0.07346 2.28367 315

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

-0.020769 -0.002814 0.000000 0.002610 0.022196

95% C onfidence Interv al for M ean -0.000816

0.000456

95% C onfidence Interv al for M edian -0.000527 9 5 % C onfidence Inter vals

0.005322

Mean Median -0.00100

-0.00075

-0.00050

-0.00025

0.00000

0.000278

95% C onfidence Interv al for S tDev

0.00025

104

0.00050

0.006224

Summary for WTI 12 M S A nderson-Darling N ormality Test

-1.2

-0.9

-0.6

-0.3

0.0

0.3

0.6

A -S quared P -V alue <

2.29 0.005

M ean S tDev V ariance S kew ness Kurtosis N

-0.12030 0.33365 0.11132 -0.73859 1.03176 315

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

-1.44546 -0.30612 -0.08027 0.09543 0.59021

95% C onfidence Interv al for M ean -0.15729

-0.08331

95% C onfidence Interv al for M edian -0.11009

-0.03873

95% C onfidence Interv al for S tDev

9 5 % C onfidence Inter vals

0.30947

0.36196

Mean Median -0.150

-0.125

-0.100

-0.075

-0.050

Summary for WTI 18 M H A nderson-Darling N ormality Test

-0.0225

-0.0150

-0.0075

-0.0000

0.0075

0.0150

0.0225

0.0300

A -S quared P -V alue <

5.63 0.005

M ean S tDev V ariance S kew ness Kurtosis N

-0.000069 0.006252 0.000039 -0.03714 3.12732 309

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

-0.022026 -0.002651 0.000000 0.002779 0.029703

95% C onfidence Interv al for M ean -0.000769

0.000631

95% C onfidence Interv al for M edian -0.000370 9 5 % C onfidence Inter vals

0.005795

Mean Median -0.00075

-0.00050

-0.00025

0.00000

0.00025

0.000478

95% C onfidence Interv al for S tDev

0.00050

105

0.006788

Summary for WTI 18 M S A nderson-Darling N ormality Test

-1.6

-1.2

-0.8

-0.4

0.0

0.4

A -S quared P -V alue <

3.24 0.005

M ean S tDev V ariance S kew ness Kurtosis N

-0.16910 0.39116 0.15300 -0.96765 1.45883 309

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

-1.80352 -0.33811 -0.13012 0.09044 0.45978

95% C onfidence Interv al for M ean -0.21288

-0.12531

95% C onfidence Interv al for M edian -0.17208

-0.06739

95% C onfidence Interv al for S tDev

9 5 % C onfidence Inter vals

0.36256

0.42470

Mean Median -0.200

-0.175

-0.150

-0.125

-0.100

-0.075

-0.050

Summary for WTI 2 Y H A nderson-Darling N ormality Test

-0.0225

-0.0150

-0.0075

0.0000

0.0075

0.0150

0.0225

A -S quared P -V alue <

7.92 0.005

M ean S tDev V ariance S kew ness Kurtosis N

0.000034 0.006872 0.000047 -0.11690 3.14997 303

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

-0.027192 -0.002677 0.000000 0.002704 0.025326

95% C onfidence Interv al for M ean -0.000743

0.000811

95% C onfidence Interv al for M edian 0.000000 9 5 % C onfidence Inter vals

0.006365

Mean Median -0.0005

0.0000

0.000490

95% C onfidence Interv al for S tDev

0.0005

0.0010

106

0.007467

Summary for WTI 2 Y S A nderson-Darling N ormality Test

-1.6

-1.2

-0.8

-0.4

0.0

A -S quared P -V alue <

6.30 0.005

M ean S tDev V ariance S kew ness Kurtosis N

-0.21736 0.43838 0.19218 -0.976625 0.613573 303

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

0.4

-1.64769 -0.43118 -0.11340 0.08760 0.55014

95% C onfidence Interv al for M ean -0.26692

-0.16780

95% C onfidence Interv al for M edian -0.16401

-0.06655

95% C onfidence Interv al for S tDev

9 5 % C onfidence Inter vals

0.40603

0.47637

Mean Median -0.25

-0.20

-0.15

-0.10

-0.05

Summary for WTI 3 Y H A nderson-Darling N ormality Test

-0.03

-0.02

-0.01

0.00

0.01

A -S quared P -V alue <

6.51 0.005

M ean S tDev V ariance S kew ness Kurtosis N

0.000282 0.006688 0.000045 -0.40523 4.71677 291

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

0.02

-0.032107 -0.002268 0.000295 0.003491 0.026624

95% C onfidence Interv al for M ean -0.000489

0.001054

95% C onfidence Interv al for M edian -0.000363 9 5 % C onfidence Inter vals

0.006185

Mean Median -0.00050

-0.00025

0.00000

0.00025

0.00050

0.000603

95% C onfidence Interv al for S tDev

0.00075

107

0.00100

0.007281

Summary for WTI 3 Y S A nderson-Darling N ormality Test

-1.5

-1.2

-0.9

-0.6

-0.3

0.0

A -S quared P -V alue <

5.21 0.005

M ean S tDev V ariance S kew ness Kurtosis N

-0.31565 0.47940 0.22983 -0.714809 -0.262354 291

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

0.3

-1.61448 -0.64097 -0.20190 0.03991 0.49639

95% C onfidence Interv al for M ean -0.37097

-0.26034

95% C onfidence Interv al for M edian -0.28127

-0.13125

95% C onfidence Interv al for S tDev

9 5 % C onfidence Inter vals

0.44336

0.52187

Mean Median -0.40

-0.35

-0.30

-0.25

-0.20

-0.15

-0.10

Summary for WTI 5 Y H A nderson-Darling N ormality Test

-0.0225

-0.0150

-0.0075

0.0000

0.0075

0.0150

0.0225

A -S quared P -V alue <

4.78 0.005

M ean S tDev V ariance S kew ness Kurtosis N

0.000769 0.006805 0.000046 0.03117 2.59202 267

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

-0.025294 -0.001775 0.000861 0.003839 0.028322

95% C onfidence Interv al for M ean -0.000051

0.001589

95% C onfidence Interv al for M edian 0.000491 9 5 % C onfidence Inter vals

0.006273

Mean Median 0.00000

0.00025

0.00050

0.00075

0.00100

0.001184

95% C onfidence Interv al for S tDev

0.00125

108

0.00150

0.007437

Summary for WTI 5 Y S A nderson-Darling N ormality Test

-3.0

-2.4

-1.8

-1.2

-0.6

0.0

0.6

A -S quared P -V alue <

9.08 0.005

M ean S tDev V ariance S kew ness Kurtosis N

-0.53782 0.71411 0.50996 -1.50155 2.65093 267

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

-3.46105 -0.84217 -0.34044 -0.02238 0.51637

95% C onfidence Interv al for M ean -0.62387

-0.45177

95% C onfidence Interv al for M edian -0.44477

-0.28047

95% C onfidence Interv al for S tDev

9 5 % C onfidence Inter vals

0.65825

0.78043

Mean Median -0.60

-0.55

-0.50

-0.45

-0.40

-0.35

-0.30

Summary for LME 1 M H A nderson-Darling N ormality Test

-0.08

-0.04

-0.00

A -S quared P -V alue

0.64 0.090

M ean S tDev V ariance S kew ness Kurtosis N

0.001425 0.027910 0.000779 -0.90404 2.84052 56

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

0.04

-0.102338 -0.011704 -0.000405 0.018810 0.063008

95% C onfidence Interv al for M ean -0.006049

0.008900

95% C onfidence Interv al for M edian -0.006577 9 5 % C onfidence Inter vals

0.023530

Mean Median -0.005

0.000

0.011555

95% C onfidence Interv al for S tDev

0.005

0.010

109

0.034308

Summary for LME 1 M S A nderson-Darling N ormality Test

-0.1

-0.0

0.1

0.2

0.3

A -S quared P -V alue <

1.49 0.005

M ean S tDev V ariance S kew ness Kurtosis N

0.019833 0.097378 0.009483 1.71409 5.58883 56

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

0.4

-0.170765 -0.041253 0.013852 0.059633 0.425704

95% C onfidence Interv al for M ean -0.006245

0.045911

95% C onfidence Interv al for M edian -0.022848

0.036929

95% C onfidence Interv al for S tDev

9 5 % C onfidence Inter vals

0.082097

0.119703

Mean Median -0.02

0.00

0.02

0.04

Summary for LME 3 M H A nderson-Darling N ormality Test

-0.08

-0.04

0.00

0.04

0.08

A -S quared P -V alue

0.44 0.288

M ean S tDev V ariance S kew ness Kurtosis N

0.005463 0.041064 0.001686 -0.095924 -0.181588 54

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

-0.083152 -0.014617 0.007481 0.030972 0.087616

95% C onfidence Interv al for M ean -0.005745

0.016672

95% C onfidence Interv al for M edian -0.004003 9 5 % C onfidence Inter vals

0.034519

Mean Median -0.005

0.000

0.005

0.010

0.015

0.021698

95% C onfidence Interv al for S tDev

0.020

110

0.025

0.050694

Summary for LME 3 M S A nderson-Darling N ormality Test

-0.2

-0.0

0.2

0.4

A -S quared P -V alue

1.10 0.006

M ean S tDev V ariance S kew ness Kurtosis N

0.037112 0.187957 0.035328 1.13453 2.87892 54

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

0.6

-0.334767 -0.080666 0.045800 0.109938 0.646239

95% C onfidence Interv al for M ean -0.014190

0.088415

95% C onfidence Interv al for M edian -0.038119

0.079355

95% C onfidence Interv al for S tDev

9 5 % C onfidence Inter vals

0.158001

0.232035

Mean Median -0.050

-0.025

0.000

0.025

0.050

0.075

0.100

Summary for LME 6 M H A nderson-Darling N ormality Test

-0.08

-0.04

-0.00

0.04

0.08

A -S quared P -V alue

0.29 0.589

M ean S tDev V ariance S kew ness Kurtosis N

0.008123 0.041924 0.001758 -0.230972 -0.493799 51

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

-0.090913 -0.019791 0.015843 0.039505 0.091059

95% C onfidence Interv al for M ean -0.003668

0.019915

95% C onfidence Interv al for M edian -0.009854 9 5 % C onfidence Inter vals

0.035079

Mean Median -0.01

0.00

0.01

0.024338

95% C onfidence Interv al for S tDev

0.02

111

0.052115

Summary for LME 6 M S A nderson-Darling N ormality Test

-0.3

0.0

0.3

0.6

A -S quared P -V alue

0.90 0.020

M ean S tDev V ariance S kew ness Kurtosis N

0.031551 0.240654 0.057914 0.578030 0.545435 51

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

-0.521719 -0.118882 -0.046802 0.194250 0.672897

95% C onfidence Interv al for M ean -0.036134

0.099236

95% C onfidence Interv al for M edian -0.095610

0.115522

95% C onfidence Interv al for S tDev

9 5 % C onfidence Inter vals

0.201358

0.299152

Mean Median -0.10

-0.05

0.00

0.05

0.10

Summary for LME 12 M H A nderson-Darling N ormality Test

-0.08

-0.04

0.00

0.04

0.08

A -S quared P -V alue

0.26 0.713

M ean S tDev V ariance S kew ness Kurtosis N

0.013265 0.043692 0.001909 -0.216229 -0.511474 45

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

-0.084678 -0.017362 0.012463 0.043219 0.083983

95% C onfidence Interv al for M ean 0.000138

0.026391

95% C onfidence Interv al for M edian -0.000099 9 5 % C onfidence Inter vals

0.036170

Mean Median 0.000

0.005

0.010

0.015

0.020

0.031303

95% C onfidence Interv al for S tDev

0.025

112

0.030

0.055191

Summary for LME 12 M S A nderson-Darling N ormality Test

-0.6

-0.4

-0.2

-0.0

0.2

0.4

0.6

A -S quared P -V alue

1.03 0.009

M ean S tDev V ariance S kew ness Kurtosis N

-0.011292 0.340891 0.116207 0.194727 -0.940415 45

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

-0.674838 -0.251577 -0.083364 0.359959 0.634010

95% C onfidence Interv al for M ean -0.113707

0.091123

95% C onfidence Interv al for M edian -0.202100

0.081042

95% C onfidence Interv al for S tDev

9 5 % C onfidence Inter vals

0.282208

0.430614

Mean Median -0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

Summary for LME 18 M H A nderson-Darling N ormality Test

-0.10

-0.05

0.00

0.05

0.10

A -S quared P -V alue

0.38 0.378

M ean S tDev V ariance S kew ness Kurtosis N

0.011860 0.052330 0.002738 -0.393328 -0.237181 39

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

-0.098961 -0.009228 0.011003 0.055611 0.103306

95% C onfidence Interv al for M ean -0.005104

0.028823

95% C onfidence Interv al for M edian -0.002173 9 5 % C onfidence Inter vals

0.042767

Mean Median -0.01

0.00

0.01

0.029439

95% C onfidence Interv al for S tDev

0.02

0.03

113

0.067442

Summary for LME 18 M S A nderson-Darling N ormality Test

-0.6

-0.3

0.0

0.3

0.6

A -S quared P -V alue <

1.57 0.005

M ean S tDev V ariance S kew ness Kurtosis N

-0.073532 0.398675 0.158942 0.38894 -1.40689 39

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

-0.550943 -0.455715 -0.165604 0.325997 0.591994

95% C onfidence Interv al for M ean -0.202768

0.055703

95% C onfidence Interv al for M edian -0.375822

0.127748

95% C onfidence Interv al for S tDev

9 5 % C onfidence Inter vals

0.325816

0.513804

Mean Median -0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

Summary for LME 2 Y H A nderson-Darling N ormality Test

-0.10

-0.05

0.00

0.05

0.10

A -S quared P -V alue

0.43 0.297

M ean S tDev V ariance S kew ness Kurtosis N

0.005245 0.050443 0.002544 -0.312725 -0.094129 33

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

-0.112931 -0.023976 0.010878 0.036685 0.089787

95% C onfidence Interv al for M ean -0.012641

0.023131

95% C onfidence Interv al for M edian -0.005852 9 5 % C onfidence Inter vals

0.040565

Mean Median -0.01

0.00

0.018869

95% C onfidence Interv al for S tDev

0.01

0.02

114

0.066720

Summary for LME 2 Y S A nderson-Darling N ormality Test

-0.8

-0.4

0.0

0.4

A -S quared P -V alue <

1.20 0.005

M ean S tDev V ariance S kew ness Kurtosis N

-0.12515 0.45324 0.20543 0.10388 -1.57731 33

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

-0.80396 -0.55048 -0.14421 0.26919 0.57140

95% C onfidence Interv al for M ean -0.28586

0.03556

95% C onfidence Interv al for M edian -0.51114

0.18476

95% C onfidence Interv al for S tDev

9 5 % C onfidence Inter vals

0.36449

0.59950

Mean Median -0.6

-0.4

-0.2

0.0

0.2

Summary for LME 3 Y H A nderson-Darling N ormality Test

-0.025

0.000

0.025

0.050

0.075

0.100

A -S quared P -V alue

0.26 0.669

M ean S tDev V ariance S kew ness Kurtosis N

0.023482 0.042511 0.001807 0.477689 -0.387708 21

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

-0.036670 -0.008471 0.018228 0.056049 0.108759

95% C onfidence Interv al for M ean 0.004131

0.042833

95% C onfidence Interv al for M edian -0.006090 9 5 % C onfidence Inter vals

0.032524

Mean Median 0.00

0.01

0.02

0.03

0.045850

95% C onfidence Interv al for S tDev

0.04

115

0.05

0.061389

Summary for LME 3 Y S A nderson-Darling N ormality Test

-0.6

-0.4

-0.2

-0.0

0.2

0.4

0.6

A -S quared P -V alue

0.89 0.019

M ean S tDev V ariance S kew ness Kurtosis N

-0.086707 0.393332 0.154710 -0.18309 -1.51848 21

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

-0.646694 -0.538813 0.031389 0.250295 0.524426

95% C onfidence Interv al for M ean -0.265749

0.092336

95% C onfidence Interv al for M edian -0.518251

0.166049

95% C onfidence Interv al for S tDev

9 5 % C onfidence Inter vals

0.300922

0.567998

Mean Median -0.6

-0.4

-0.2

0.0

0.2

Summary for HRC 1 M H A nderson-Darling N ormality Test

-0.16

-0.08

0.00

0.08

A -S quared P -V alue

0.36 0.428

M ean S tDev V ariance S kew ness Kurtosis N

0.009308 0.065406 0.004278 0.434119 0.725240 53

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

0.16

-0.141669 -0.032721 0.012066 0.048396 0.178051

95% C onfidence Interv al for M ean -0.008720

0.027336

95% C onfidence Interv al for M edian -0.013410 9 5 % C onfidence Inter vals

0.054898

Mean Median -0.01

0.00

0.01

0.022302

95% C onfidence Interv al for S tDev

0.02

116

0.03

0.080925

Summary for HRC 1 M S A nderson-Darling N ormality Test

-0.2

-0.1

0.0

0.1

0.2

A -S quared P -V alue

0.54 0.157

M ean S tDev V ariance S kew ness Kurtosis N

0.005206 0.087749 0.007700 -0.316071 0.711622 53

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

-0.233766 -0.059619 0.011745 0.066602 0.221557

95% C onfidence Interv al for M ean -0.018980

0.029393

95% C onfidence Interv al for M edian -0.005119

0.043058

95% C onfidence Interv al for S tDev

9 5 % C onfidence Inter vals

0.073652

0.108569

Mean Median -0.02

-0.01

0.00

0.01

0.02

0.03

0.04

Summary for HRC 3 M H A nderson-Darling N ormality Test

-0.24

-0.12

-0.00

0.12

A -S quared P -V alue <

1.40 0.005

M ean S tDev V ariance S kew ness Kurtosis N

0.020960 0.108384 0.011747 0.27862 2.02968 51

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

0.24

-0.290461 -0.019744 0.018507 0.055885 0.316213

95% C onfidence Interv al for M ean -0.009524

0.051443

95% C onfidence Interv al for M edian 0.001671 9 5 % C onfidence Inter vals

0.090686

Mean Median 0.000

0.015

0.030

0.044598

95% C onfidence Interv al for S tDev

0.045

117

0.060

0.134730

Summary for HRC 3 M S A nderson-Darling N ormality Test

-0.4

-0.2

0.0

0.2

0.4

A -S quared P -V alue

0.56 0.138

M ean S tDev V ariance S kew ness Kurtosis N

0.003151 0.198830 0.039533 -0.509049 0.791313 51

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

-0.528889 -0.124260 0.025532 0.108998 0.442359

95% C onfidence Interv al for M ean -0.052771

0.059073

95% C onfidence Interv al for M edian -0.012921

0.072394

95% C onfidence Interv al for S tDev

9 5 % C onfidence Inter vals

0.166363

0.247162

Mean Median -0.050

-0.025

0.000

0.025

0.050

0.075

Summary for HRC 6 M H A nderson-Darling N ormality Test

-0.2

-0.1

-0.0

0.1

0.2

A -S quared P -V alue <

1.22 0.005

M ean S tDev V ariance S kew ness Kurtosis N

0.024096 0.119414 0.014260 0.91321 1.47958 48

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

0.3

-0.208481 -0.051234 0.011587 0.074319 0.351351

95% C onfidence Interv al for M ean -0.010578

0.058770

95% C onfidence Interv al for M edian -0.008904 9 5 % C onfidence Inter vals

0.099408

Mean Median 0.000

0.015

0.030

0.038966

95% C onfidence Interv al for S tDev

0.045

118

0.060

0.149575

Summary for HRC 6 M S A nderson-Darling N ormality Test

-0.4

-0.2

0.0

0.2

0.4

A -S quared P -V alue

0.45 0.263

M ean S tDev V ariance S kew ness Kurtosis N

-0.017660 0.260765 0.067998 0.035365 -0.185530 48

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

-0.541096 -0.226141 0.016081 0.136648 0.546917

95% C onfidence Interv al for M ean -0.093378

0.058059

95% C onfidence Interv al for M edian -0.057795

0.070665

95% C onfidence Interv al for S tDev

9 5 % C onfidence Inter vals

0.217079

0.326629

Mean Median -0.10

-0.05

0.00

0.05

Summary for HRC 12 M H A nderson-Darling N ormality Test

-0.4

-0.2

0.0

0.2

0.4

A -S quared P -V alue <

1.38 0.005

M ean S tDev V ariance S kew ness Kurtosis N

0.021999 0.128664 0.016554 0.09911 2.49271 42

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

-0.363541 -0.041624 0.020887 0.060749 0.365984

95% C onfidence Interv al for M ean -0.018095

0.062094

95% C onfidence Interv al for M edian -0.002400 9 5 % C onfidence Inter vals

0.105865

Mean Median -0.02

0.00

0.02

0.044006

95% C onfidence Interv al for S tDev

0.04

0.06

119

0.164068

Summary for HRC 12 M S A nderson-Darling N ormality Test

-0.6

-0.4

-0.2

0.0

0.2

0.4

A -S quared P -V alue

0.46 0.252

M ean S tDev V ariance S kew ness Kurtosis N

-0.067128 0.280393 0.078620 -0.583510 0.237223 42

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

-0.743902 -0.209396 -0.050808 0.144598 0.420000

95% C onfidence Interv al for M ean -0.154505

0.020248

95% C onfidence Interv al for M edian -0.144955

0.089061

95% C onfidence Interv al for S tDev

9 5 % C onfidence Inter vals

0.230709

0.357547

Mean Median -0.15

-0.10

-0.05

0.00

0.05

0.10

Summary for HRC 18 M H A nderson-Darling N ormality Test

-0.2

-0.1

-0.0

0.1

0.2

0.3

A -S quared P -V alue

0.67 0.072

M ean S tDev V ariance S kew ness Kurtosis N

0.026076 0.115679 0.013382 0.63970 1.66424 36

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

-0.209711 -0.040027 0.024479 0.079679 0.359667

95% C onfidence Interv al for M ean -0.013064

0.065217

95% C onfidence Interv al for M edian -0.013460 9 5 % C onfidence Inter vals

0.093825

Mean Median 0.00

0.02

0.049572

95% C onfidence Interv al for S tDev

0.04

0.06

120

0.150896

Summary for HRC 18 M S A nderson-Darling N ormality Test

-0.8

-0.6

-0.4

-0.2

0.0

A -S quared P -V alue

0.69 0.066

M ean S tDev V ariance S kew ness Kurtosis N

-0.11391 0.32236 0.10392 -0.828644 0.179460 36

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

0.2

-0.91919 -0.25887 -0.09864 0.13657 0.33989

95% C onfidence Interv al for M ean -0.22298

-0.00484

95% C onfidence Interv al for M edian -0.16891

0.06949

95% C onfidence Interv al for S tDev

9 5 % C onfidence Inter vals

0.26146

0.42050

Mean Median -0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

Summary for HRC 2 Y H A nderson-Darling N ormality Test

-0.2

-0.1

-0.0

0.1

0.2

0.3

0.4

A -S quared P -V alue <

1.16 0.005

M ean S tDev V ariance S kew ness Kurtosis N

0.030476 0.133783 0.017898 1.12816 1.56327 30

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

-0.223978 -0.053811 0.004743 0.072969 0.386732

95% C onfidence Interv al for M ean -0.019480

0.080431

95% C onfidence Interv al for M edian -0.048640 9 5 % C onfidence Inter vals

0.106546

Mean Median -0.050

-0.025

0.000

0.025

0.059162

95% C onfidence Interv al for S tDev

0.050

121

0.075

0.179847

Summary for HRC 2 Y S A nderson-Darling N ormality Test

-0.8

-0.6

-0.4

-0.2

0.0

0.2

A -S quared P -V alue

0.63 0.090

M ean S tDev V ariance S kew ness Kurtosis N

-0.17558 0.38506 0.14827 -0.514658 -0.609671 30

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

0.4

-0.89349 -0.39430 -0.10990 0.10487 0.40000

95% C onfidence Interv al for M ean -0.31937

-0.03180

95% C onfidence Interv al for M edian -0.28413

0.01355

95% C onfidence Interv al for S tDev

9 5 % C onfidence Inter vals

0.30666

0.51764

Mean Median -0.30

-0.25

-0.20

-0.15

-0.10

-0.05

0.00

Summary for HRC 3 Y H A nderson-Darling N ormality Test

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

A -S quared P -V alue

0.57 0.118

M ean S tDev V ariance S kew ness Kurtosis N

0.060171 0.155538 0.024192 0.686578 -0.175261 18

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

-0.175349 -0.036818 0.029029 0.118935 0.352454

95% C onfidence Interv al for M ean -0.017176

0.137519

95% C onfidence Interv al for M edian -0.033396 9 5 % C onfidence Inter vals

0.116714

Mean Median -0.05

0.00

0.05

0.098450

95% C onfidence Interv al for S tDev

0.10

122

0.15

0.233173

Summary for HRC 3 Y S A nderson-Darling N ormality Test

-0.6

-0.4

-0.2

0.0

A -S quared P -V alue

0.28 0.597

M ean S tDev V ariance S kew ness Kurtosis N

-0.18261 0.27493 0.07559 0.424221 -0.694416 18

M inimum 1st Q uartile M edian 3rd Q uartile M aximum

0.2

-0.55303 -0.42147 -0.19972 -0.00671 0.33375

95% C onfidence Interv al for M ean -0.31933

-0.04589

95% C onfidence Interv al for M edian -0.40329 9 5 % C onfidence Inter vals

0.20630

Mean Median -0.4

-0.3

-0.2

-0.01897

95% C onfidence Interv al for S tDev

-0.1

123

0.0

0.41216

APPENDIX C: Statistical Tests

C.1 EQUAL VARIANCE TESTS

Test for Equal Variances: WTI 1 M H, WTI 1 M S 95% Bonferroni confidence intervals for standard deviations N Lower StDev Upper WTI 1 M H 326 0.0043159 0.0046963 0.0051471 WTI 1 M S 326 0.0797589 0.0867878 0.0951201 F-Test (Normal Distribution) Test statistic = 0.00, p-value = 0.000 Levene's Test (Any Continuous Distribution) Test statistic = 346.61, p-value = 0.000

Test for Equal Variances: WTI 3 M H, WTI 3 M S 95% Bonferroni confidence intervals for standard deviations N Lower StDev Upper WTI 3 M H 324 0.004741 0.005160 0.005658 WTI 3 M S 324 0.158720 0.172750 0.189392 F-Test (Normal Distribution) Test statistic = 0.00, p-value = 0.000 Levene's Test (Any Continuous Distribution) Test statistic = 320.97, p-value = 0.000

124

Test for Equal Variances: WTI 6 M H, WTI 6 M S 95% Bonferroni confidence intervals for standard deviations N Lower StDev Upper WTI 6 M H 321 0.005365 0.005841 0.006407 WTI 6 M S 321 0.219978 0.239515 0.262706 F-Test (Normal Distribution) Test statistic = 0.00, p-value = 0.000 Levene's Test (Any Continuous Distribution) Test statistic = 437.13, p-value = 0.000

Test for Equal Variances: WTI 12 M H, WTI 12 M S 95% Bonferroni confidence intervals for standard deviations N Lower StDev Upper WTI 12 M H 315 0.005265 0.005737 0.006299 WTI 12 M S 315 0.306196 0.333650 0.366290 F-Test (Normal Distribution) Test statistic = 0.00, p-value = 0.000 Levene's Test (Any Continuous Distribution) Test statistic = 411.65, p-value = 0.000

Test for Equal Variances: WTI 18 M H, WTI 18 M S 95% Bonferroni confidence intervals for standard deviations N Lower StDev Upper WTI 18 M H 309 0.005733 0.006252 0.006870 WTI 18 M S 309 0.358685 0.391158 0.429830 F-Test (Normal Distribution) Test statistic = 0.00, p-value = 0.000 Levene's Test (Any Continuous Distribution) Test statistic = 390.01, p-value = 0.000

125

Test for Equal Variances: WTI 2 Y H, WTI 2 Y S 95% Bonferroni confidence intervals for standard deviations N Lower StDev Upper WTI 2 Y H 303 0.006296 0.006872 0.007559 WTI 2 Y S 303 0.401657 0.438379 0.482189 F-Test (Normal Distribution) Test statistic = 0.00, p-value = 0.000 Levene's Test (Any Continuous Distribution) Test statistic = 349.06, p-value = 0.000

Test for Equal Variances: WTI 3 Y H, WTI 3 Y S 95% Bonferroni confidence intervals for standard deviations N Lower StDev Upper WTI 3 Y H 291 0.006117 0.006688 0.007372 WTI 3 Y S 291 0.438491 0.479404 0.528391 F-Test (Normal Distribution) Test statistic = 0.00, p-value = 0.000 Levene's Test (Any Continuous Distribution) Test statistic = 428.88, p-value = 0.000

Test for Equal Variances: WTI 5 Y H, WTI 5 Y S 95% Bonferroni confidence intervals for standard deviations N Lower StDev Upper WTI 5 Y H 267 0.006201 0.006805 0.007534 WTI 5 Y S 267 0.650715 0.714114 0.790633 F-Test (Normal Distribution) Test statistic = 0.00, p-value = 0.000 Levene's Test (Any Continuous Distribution) Test statistic = 244.96, p-value = 0.000

126

Test for Equal Variances: LME 1 M H, LME 1 M S 95% Bonferroni confidence intervals for standard deviations N Lower StDev Upper LME 1 M H 56 0.0229788 0.0279096 0.035387 LME 1 M S 56 0.0801746 0.0973783 0.123468 F-Test (Normal Distribution) Test statistic = 0.08, p-value = 0.000 Levene's Test (Any Continuous Distribution) Test statistic = 25.60, p-value = 0.000

Test for Equal Variances: LME 3 M H, LME 3 M S 95% Bonferroni confidence intervals for standard deviations N Lower StDev Upper LME 3 M H 54 0.033698 0.041064 0.052323 LME 3 M S 54 0.154241 0.187957 0.239494 F-Test (Normal Distribution) Test statistic = 0.05, p-value = 0.000 Levene's Test (Any Continuous Distribution) Test statistic = 32.25, p-value = 0.000

Test for Equal Variances: LME 6 M H, LME 6 M S 95% Bonferroni confidence intervals for standard deviations N Lower StDev Upper LME 6 M H 51 0.034222 0.041924 0.053849 LME 6 M S 51 0.196442 0.240654 0.309104 F-Test (Normal Distribution) Test statistic = 0.03, p-value = 0.000 Levene's Test (Any Continuous Distribution) Test statistic = 43.04, p-value = 0.000

127

Test for Equal Variances: LME 12 M H, LME 12 M S 95% Bonferroni confidence intervals for standard deviations N Lower StDev Upper LME 12 M H 45 0.035237 0.043692 0.057172 LME 12 M S 45 0.274928 0.340891 0.446070 F-Test (Normal Distribution) Test statistic = 0.02, p-value = 0.000 Levene's Test (Any Continuous Distribution) Test statistic = 71.86, p-value = 0.000

Test for Equal Variances: LME 18 M H, LME 18 M S 95% Bonferroni confidence intervals for standard deviations N Lower StDev Upper LME 18 M H 39 0.041592 0.052330 0.070086 LME 18 M S 39 0.316865 0.398675 0.533947 F-Test (Normal Distribution) Test statistic = 0.02, p-value = 0.000 Levene's Test (Any Continuous Distribution) Test statistic = 78.86, p-value = 0.000

Test for Equal Variances: LME 2 Y H, LME 2 Y S 95% Bonferroni confidence intervals for standard deviations N Lower StDev Upper LME 2 Y H 33 0.039367 0.050443 0.069626 LME 2 Y S 33 0.353723 0.453242 0.625609 F-Test (Normal Distribution) Test statistic = 0.01, p-value = 0.000 Levene's Test (Any Continuous Distribution) Test statistic = 139.19, p-value = 0.000

128

Test for Equal Variances: LME 3 Y H, LME 3 Y S 95% Bonferroni confidence intervals for standard deviations N Lower StDev Upper LME 3 Y H 21 0.031357 0.042511 0.064995 LME 3 Y S 21 0.290124 0.393332 0.601355 F-Test (Normal Distribution) Test statistic = 0.01, p-value = 0.000 Levene's Test (Any Continuous Distribution) Test statistic = 33.45, p-value = 0.000

Test for Equal Variances: HRC 1 M H, HRC 1 M S 95% Bonferroni confidence intervals for standard deviations N Lower StDev Upper HRC 1 M H 53 0.0535809 0.0654056 0.083555 HRC 1 M S 53 0.0718847 0.0877487 0.112099 F-Test (Normal Distribution) Test statistic = 0.56, p-value = 0.036 Levene's Test (Any Continuous Distribution) Test statistic = 3.17, p-value = 0.078

Test for Equal Variances: HRC 3 M H, HRC 3 M S 95% Bonferroni confidence intervals for standard deviations N Lower StDev Upper HRC 3 M H 51 0.088472 0.108384 0.139212 HRC 3 M S 51 0.162302 0.198830 0.255384 F-Test (Normal Distribution) Test statistic = 0.30, p-value = 0.000 Levene's Test (Any Continuous Distribution) Test statistic = 11.35, p-value = 0.001

129

Test for Equal Variances: HRC 6 M H, HRC 6 M S 95% Bonferroni confidence intervals for standard deviations N Lower StDev Upper HRC 6 M H 48 0.096916 0.119414 0.154737 HRC 6 M S 48 0.211636 0.260765 0.337901 F-Test (Normal Distribution) Test statistic = 0.21, p-value = 0.000 Levene's Test (Any Continuous Distribution) Test statistic = 18.86, p-value = 0.000

Test for Equal Variances: HRC 12 M H, HRC 12 M S 95% Bonferroni confidence intervals for standard deviations N Lower StDev Upper HRC 12 M H 42 0.103050 0.128664 0.170211 HRC 12 M S 42 0.224573 0.280393 0.370936 F-Test (Normal Distribution) Test statistic = 0.21, p-value = 0.000 Levene's Test (Any Continuous Distribution) Test statistic = 19.58, p-value = 0.000

Test for Equal Variances: HRC 18 M H, HRC 18 M S 95% Bonferroni confidence intervals for standard deviations N Lower StDev Upper HRC 18 M H 36 0.091156 0.115679 0.157116 HRC 18 M S 36 0.254024 0.322362 0.437833 F-Test (Normal Distribution) Test statistic = 0.13, p-value = 0.000 Levene's Test (Any Continuous Distribution) Test statistic = 22.29, p-value = 0.000

130

Test for Equal Variances: HRC 2 Y H, HRC 2 Y S 95% Bonferroni confidence intervals for standard deviations N Lower StDev Upper HRC 2 Y H 30 0.103265 0.133783 0.188173 HRC 2 Y S 30 0.297219 0.385057 0.541604 F-Test (Normal Distribution) Test statistic = 0.12, p-value = 0.000 Levene's Test (Any Continuous Distribution) Test statistic = 16.91, p-value = 0.000

Test for Equal Variances: HRC 3 Y H, HRC 3 Y S 95% Bonferroni confidence intervals for standard deviations N Lower StDev Upper HRC 3 Y H 18 0.112242 0.155538 0.248429 HRC 3 Y S 18 0.198399 0.274927 0.439122 F-Test (Normal Distribution) Test statistic = 0.32, p-value = 0.024 Levene's Test (Any Continuous Distribution) Test statistic = 6.88, p-value = 0.013

C.2 MEAN TESTS Two-Sample T-Test and CI: WTI 1 M H, WTI 1 M S Two-sample T for WTI 1 M H vs WTI 1 M S N Mean StDev SE Mean WTI 1 M H 326 -0.00005 0.00470 0.00026 WTI 1 M S 326 -0.0080 0.0868 0.0048 Difference = mu (WTI 1 M H) - mu (WTI 1 M S) Estimate for difference: 0.00794 95% CI for difference: (-0.00153, 0.01741) T-Test of difference = 0 (vs not =): T-Value = 1.65 P-Value = 0.100 DF = 326 131

Two-Sample T-Test and CI: WTI 3 M H, WTI 3 M S Two-sample T for WTI 3 M H vs WTI 3 M S N Mean StDev SE Mean WTI 3 M H 324 -0.00009 0.00516 0.00029 WTI 3 M S 324 -0.031 0.173 0.0096 Difference = mu (WTI 3 M H) - mu (WTI 3 M S) Estimate for difference: 0.03069 95% CI for difference: (0.01180, 0.04958) T-Test of difference = 0 (vs not =): T-Value = 3.20 P-Value = 0.002 DF = 323

Two-Sample T-Test and CI: WTI 6 M H, WTI 6 M S Two-sample T for WTI 6 M H vs WTI 6 M S N Mean StDev SE Mean WTI 6 M H 321 -0.00017 0.00584 0.00033 WTI 6 M S 321 -0.063 0.240 0.013 Difference = mu (WTI 6 M H) - mu (WTI 6 M S) Estimate for difference: 0.0627 95% CI for difference: (0.0364, 0.0891) T-Test of difference = 0 (vs not =): T-Value = 4.69 P-Value = 0.000 DF = 320

Two-Sample T-Test and CI: WTI 12 M H, WTI 12 M S Two-sample T for WTI 12 M H vs WTI 12 M S N Mean StDev SE Mean WTI 12 M H 315 -0.00018 0.00574 0.00032 WTI 12 M S 315 -0.120 0.334 0.019 Difference = mu (WTI 12 M H) - mu (WTI 12 M S) Estimate for difference: 0.1201 95% CI for difference: (0.0831, 0.1571) T-Test of difference = 0 (vs not =): T-Value = 6.39 P-Value = 0.000 DF = 314

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Two-Sample T-Test and CI: WTI 18 M H, WTI 18 M S Two-sample T for WTI 18 M H vs WTI 18 M S N Mean StDev SE Mean WTI 18 M H 309 -0.00007 0.00625 0.00036 WTI 18 M S 309 -0.169 0.391 0.022 Difference = mu (WTI 18 M H) - mu (WTI 18 M S) Estimate for difference: 0.1690 95% CI for difference: (0.1252, 0.2128) T-Test of difference = 0 (vs not =): T-Value = 7.59 P-Value = 0.000 DF = 308

Two-Sample T-Test and CI: WTI 2 Y H, WTI 2 Y S Two-sample T for WTI 2 Y H vs WTI 2 Y S N Mean StDev SE Mean WTI 2 Y H 303 0.00003 0.00687 0.00039 WTI 2 Y S 303 -0.217 0.438 0.025 Difference = mu (WTI 2 Y H) - mu (WTI 2 Y S) Estimate for difference: 0.2174 95% CI for difference: (0.1678, 0.2670) T-Test of difference = 0 (vs not =): T-Value = 8.63 P-Value = 0.000 DF = 302

Two-Sample T-Test and CI: WTI 3 Y H, WTI 3 Y S Two-sample T for WTI 3 Y H vs WTI 3 Y S N Mean StDev SE Mean WTI 3 Y H 291 0.00028 0.00669 0.00039 WTI 3 Y S 291 -0.316 0.479 0.028

Difference = mu (WTI 3 Y H) - mu (WTI 3 Y S) Estimate for difference: 0.3159 95% CI for difference: (0.2606, 0.3713) T-Test of difference = 0 (vs not =): T-Value = 11.24 P-Value = 0.000 DF = 290

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Two-Sample T-Test and CI: WTI 5 Y H, WTI 5 Y S Two-sample T for WTI 5 Y H vs WTI 5 Y S N Mean StDev SE Mean WTI 5 Y H 267 0.00077 0.00681 0.00042 WTI 5 Y S 267 -0.538 0.714 0.044 Difference = mu (WTI 5 Y H) - mu (WTI 5 Y S) Estimate for difference: 0.5386 95% CI for difference: (0.4525, 0.6246) T-Test of difference = 0 (vs not =): T-Value = 12.32 P-Value = 0.000 DF = 266

Two-Sample T-Test and CI: LME 1 M H, LME 1 M S Two-sample T for LME 1 M H vs LME 1 M S N Mean StDev SE Mean LME 1 M H 56 0.0014 0.0279 0.0037 LME 1 M S 56 0.0198 0.0974 0.013 Difference = mu (LME 1 M H) - mu (LME 1 M S) Estimate for difference: -0.0184 95% CI for difference: (-0.0455, 0.0086) T-Test of difference = 0 (vs not =): T-Value = -1.36 P-Value = 0.179 DF = 63

Two-Sample T-Test and CI: LME 3 M H, LME 3 M S Two-sample T for LME 3 M H vs LME 3 M S N Mean StDev SE Mean LME 3 M H 54 0.0055 0.0411 0.0056 LME 3 M S 54 0.037 0.188 0.026 Difference = mu (LME 3 M H) - mu (LME 3 M S) Estimate for difference: -0.0316 95% CI for difference: (-0.0841, 0.0208) T-Test of difference = 0 (vs not =): T-Value = -1.21 P-Value = 0.232 DF = 58

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Two-Sample T-Test and CI: LME 6 M H, LME 6 M S Two-sample T for LME 6 M H vs LME 6 M S N Mean StDev SE Mean LME 6 M H 51 0.0081 0.0419 0.0059 LME 6 M S 51 0.032 0.241 0.034 Difference = mu (LME 6 M H) - mu (LME 6 M S) Estimate for difference: -0.0234 95% CI for difference: (-0.0920, 0.0452) T-Test of difference = 0 (vs not =): T-Value = -0.68 P-Value = 0.496 DF = 53

Two-Sample T-Test and CI: LME 12 M H, LME 12 M S Two-sample T for LME 12 M H vs LME 12 M S N Mean StDev SE Mean LME 12 M H 45 0.0133 0.0437 0.0065 LME 12 M S 45 -0.011 0.341 0.051 Difference = mu (LME 12 M H) - mu (LME 12 M S) Estimate for difference: 0.0246 95% CI for difference: (-0.0786, 0.1277) T-Test of difference = 0 (vs not =): T-Value = 0.48 P-Value = 0.634 DF = 45

Two-Sample T-Test and CI: LME 18 M H, LME 18 M S Two-sample T for LME 18 M H vs LME 18 M S N Mean StDev SE Mean LME 18 M H 39 0.0119 0.0523 0.0084 LME 18 M S 39 -0.074 0.399 0.064 Difference = mu (LME 18 M H) - mu (LME 18 M S) Estimate for difference: 0.0854 95% CI for difference: (-0.0448, 0.2156) T-Test of difference = 0 (vs not =): T-Value = 1.33 P-Value = 0.192 DF = 39

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Two-Sample T-Test and CI: LME 2 Y H, LME 2 Y S Two-sample T for LME 2 Y H vs LME 2 Y S N Mean StDev SE Mean LME 2 Y H 33 0.0052 0.0504 0.0088 LME 2 Y S 33 -0.125 0.453 0.079 Difference = mu (LME 2 Y H) - mu (LME 2 Y S) Estimate for difference: 0.1304 95% CI for difference: (-0.0313, 0.2921) T-Test of difference = 0 (vs not =): T-Value = 1.64 P-Value = 0.110 DF = 32

Two-Sample T-Test and CI: LME 3 Y H, LME 3 Y S Two-sample T for LME 3 Y H vs LME 3 Y S N Mean StDev SE Mean LME 3 Y H 21 0.0235 0.0425 0.0093 LME 3 Y S 21 -0.087 0.393 0.086 Difference = mu (LME 3 Y H) - mu (LME 3 Y S) Estimate for difference: 0.1102 95% CI for difference: (-0.0699, 0.2903) T-Test of difference = 0 (vs not =): T-Value = 1.28 P-Value = 0.216 DF = 20 Two-Sample T-Test and CI: HRC 1 M H, HRC 1 M S Two-sample T for HRC 1 M H vs HRC 1 M S N Mean StDev SE Mean HRC 1 M H 53 0.0093 0.0654 0.0090 HRC 1 M S 53 0.0052 0.0877 0.012 Difference = mu (HRC 1 M H) - mu (HRC 1 M S) Estimate for difference: 0.0041 95% CI for difference: (-0.0257, 0.0339) T-Test of difference = 0 (vs not =): T-Value = 0.27 P-Value = 0.786 DF = 96

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Two-Sample T-Test and CI: HRC 3 M H, HRC 3 M S Two-sample T for HRC 3 M H vs HRC 3 M S N Mean StDev SE Mean HRC 3 M H 51 0.021 0.108 0.015 HRC 3 M S 51 0.003 0.199 0.028 Difference = mu (HRC 3 M H) - mu (HRC 3 M S) Estimate for difference: 0.0178 95% CI for difference: (-0.0453, 0.0810) T-Test of difference = 0 (vs not =): T-Value = 0.56 P-Value = 0.576 DF = 77

Two-Sample T-Test and CI: HRC 6 M H, HRC 6 M S Two-sample T for HRC 6 M H vs HRC 6 M S N Mean StDev SE Mean HRC 6 M H 48 0.024 0.119 0.017 HRC 6 M S 48 -0.018 0.261 0.038 Difference = mu (HRC 6 M H) - mu (HRC 6 M S) Estimate for difference: 0.0418 95% CI for difference: (-0.0409, 0.1244) T-Test of difference = 0 (vs not =): T-Value = 1.01 P-Value = 0.317 DF = 65

Two-Sample T-Test and CI: HRC 12 M H, HRC 12 M S Two-sample T for HRC 12 M H vs HRC 12 M S N Mean StDev SE Mean HRC 12 M H 42 0.022 0.129 0.020 HRC 12 M S 42 -0.067 0.280 0.043 Difference = mu (HRC 12 M H) - mu (HRC 12 M S) Estimate for difference: 0.0891 95% CI for difference: (-0.0062, 0.1845) T-Test of difference = 0 (vs not =): T-Value = 1.87 P-Value = 0.066 DF = 57

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Two-Sample T-Test and CI: HRC 18 M H, HRC 18 M S Two-sample T for HRC 18 M H vs HRC 18 M S N Mean StDev SE Mean HRC 18 M H 36 0.026 0.116 0.019 HRC 18 M S 36 -0.114 0.322 0.054 Difference = mu (HRC 18 M H) - mu (HRC 18 M S) Estimate for difference: 0.1400 95% CI for difference: (0.0249, 0.2551) T-Test of difference = 0 (vs not =): T-Value = 2.45 P-Value = 0.018 DF = 43

Two-Sample T-Test and CI: HRC 2 Y H, HRC 2 Y S Two-sample T for HRC 2 Y H vs HRC 2 Y S N Mean StDev SE Mean HRC 2 Y H 30 0.030 0.134 0.024 HRC 2 Y S 30 -0.176 0.385 0.070 Difference = mu (HRC 2 Y H) - mu (HRC 2 Y S) Estimate for difference: 0.2061 95% CI for difference: (0.0550, 0.3571) T-Test of difference = 0 (vs not =): T-Value = 2.77 P-Value = 0.009 DF = 35

Two-Sample T-Test and CI: HRC 3 Y H, HRC 3 Y S Two-sample T for HRC 3 Y H vs HRC 3 Y S N Mean StDev SE Mean HRC 3 Y H 18 0.060 0.156 0.037 HRC 3 Y S 18 -0.183 0.275 0.065 Difference = mu (HRC 3 Y H) - mu (HRC 3 Y S) Estimate for difference: 0.2428 95% CI for difference: (0.0897, 0.3958) T-Test of difference = 0 (vs not =): T-Value = 3.26 P-Value = 0.003 DF = 26

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C.3 MEDIAN TESTS Mann-Whitney Test and CI: WTI 1 M H, WTI 1 M S N Median WTI 1 M H 326 0.00000 WTI 1 M S 326 -0.01168 Point estimate for ETA1-ETA2 is 0.01155 95.0 Percent CI for ETA1-ETA2 is (0.00298,0.01739) W = 112797.0 Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at 0.0082 The test is significant at 0.0082 (adjusted for ties) Mann-Whitney Test and CI: WTI 3 M H, WTI 3 M S N Median WTI 3 M H 324 0.00000 WTI 3 M S 324 -0.03074 Point estimate for ETA1-ETA2 is 0.03119 95.0 Percent CI for ETA1-ETA2 is (0.01502,0.04537) W = 113735.0 Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at 0.0003 The test is significant at 0.0003 (adjusted for ties) Mann-Whitney Test and CI: WTI 6 M H, WTI 6 M S N Median WTI 6 M H 321 0.00013 WTI 6 M S 321 -0.05938 Point estimate for ETA1-ETA2 is 0.06001 95.0 Percent CI for ETA1-ETA2 is (0.02105,0.07718) W = 110396.0 Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at 0.0022 The test is significant at 0.0022 (adjusted for ties)

139

Mann-Whitney Test and CI: WTI 12 M H, WTI 12 M S N Median WTI 12 M H 315 0.00000 WTI 12 M S 315 -0.08027 Point estimate for ETA1-ETA2 is 0.07927 95.0 Percent CI for ETA1-ETA2 is (0.04395,0.10622) W = 108714.0 Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at 0.0000 The test is significant at 0.0000 (adjusted for ties) Mann-Whitney Test and CI: WTI 18 M H, WTI 18 M S N Median WTI 18 M H 309 0.00000 WTI 18 M S 309 -0.13012 Point estimate for ETA1-ETA2 is 0.12810 95.0 Percent CI for ETA1-ETA2 is (0.08362,0.16021) W = 109495.0 Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at 0.0000 The test is significant at 0.0000 (adjusted for ties) Mann-Whitney Test and CI: WTI 2 Y H, WTI 2 Y S N Median WTI 2 Y H 303 0.00000 WTI 2 Y S 303 -0.11340 Point estimate for ETA1-ETA2 is 0.11231 95.0 Percent CI for ETA1-ETA2 is (0.07574,0.15431) W = 105340.5 Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at 0.0000 The test is significant at 0.0000 (adjusted for ties) Mann-Whitney Test and CI: WTI 3 Y H, WTI 3 Y S N Median WTI 3 Y H 291 0.00030 WTI 3 Y S 291 -0.20190 Point estimate for ETA1-ETA2 is 0.20149 95.0 Percent CI for ETA1-ETA2 is (0.15062,0.24595) W = 100088.0 Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at 0.0000 The test is significant at 0.0000 (adjusted for ties)

140

Mann-Whitney Test and CI: WTI 5 Y H, WTI 5 Y S N Median WTI 5 Y H 267 0.0009 WTI 5 Y S 267 -0.3404 Point estimate for ETA1-ETA2 is 0.3426 95.0 Percent CI for ETA1-ETA2 is (0.2956,0.4279) W = 91302.5 Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at 0.0000 The test is significant at 0.0000 (adjusted for ties)

Mann-Whitney Test and CI: LME 1 M H, LME 1 M S N Median LME 1 M H 56 -0.00040 LME 1 M S 56 0.01385 Point estimate for ETA1-ETA2 is -0.00725 95.0 Percent CI for ETA1-ETA2 is (-0.03247,0.01833) W = 3081.0 Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at 0.6312

Mann-Whitney Test and CI: LME 3 M H, LME 3 M S N Median LME 3 M H 54 0.00748 LME 3 M S 54 0.04580 Point estimate for ETA1-ETA2 is -0.02732 95.0 Percent CI for ETA1-ETA2 is (-0.06800,0.02689) W = 2758.0 Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at 0.2569

Mann-Whitney Test and CI: LME 6 M H, LME 6 M S N Median LME 6 M H 51 0.0158 LME 6 M S 51 -0.0468 Point estimate for ETA1-ETA2 is 0.0384 95.0 Percent CI for ETA1-ETA2 is (-0.0802,0.0899) W = 2734.0 Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at 0.4739

141

Mann-Whitney Test and CI: LME 12 M H, LME 12 M S N Median LME 12 M H 45 0.0125 LME 12 M S 45 -0.0834 Point estimate for ETA1-ETA2 is 0.0903 95.0 Percent CI for ETA1-ETA2 is (-0.0436,0.2013) W = 2202.0 Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at 0.2140

Mann-Whitney Test and CI: LME 18 M H, LME 18 M S N Median LME 18 M H 39 0.0110 LME 18 M S 39 -0.1656 Point estimate for ETA1-ETA2 is 0.1728 95.1 Percent CI for ETA1-ETA2 is (-0.0258,0.3582) W = 1724.0 Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at 0.0674

Mann-Whitney Test and CI: LME 2 Y H, LME 2 Y S N Median LME 2 Y H 33 0.0109 LME 2 Y S 33 -0.1442 Point estimate for ETA1-ETA2 is 0.1532 95.0 Percent CI for ETA1-ETA2 is (-0.1281,0.4665) W = 1146.0 Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at 0.6080

Mann-Whitney Test and CI: LME 3 Y H, LME 3 Y S N Median LME 3 Y H 21 0.0182 LME 3 Y S 21 0.0314 Point estimate for ETA1-ETA2 is 0.0071 95.0 Percent CI for ETA1-ETA2 is (-0.1327,0.4944) W = 457.0 Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at 0.8999

142

Mann-Whitney Test and CI: HRC 1 M H, HRC 1 M S N Median HRC 1 M H 53 0.01207 HRC 1 M S 53 0.01174 Point estimate for ETA1-ETA2 is -0.00303 95.1 Percent CI for ETA1-ETA2 is (-0.03097,0.02573) W = 2803.5 Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at 0.8422 The test is significant at 0.8422 (adjusted for ties)

Mann-Whitney Test and CI: HRC 3 M H, HRC 3 M S N Median HRC 3 M H 51 0.01851 HRC 3 M S 51 0.02553 Point estimate for ETA1-ETA2 is -0.00165 95.0 Percent CI for ETA1-ETA2 is (-0.05244,0.05394) W = 2613.0 Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at 0.9307

Mann-Whitney Test and CI: HRC 6 M H, HRC 6 M S N Median HRC 6 M H 48 0.0116 HRC 6 M S 48 0.0161 Point estimate for ETA1-ETA2 is 0.0173 95.0 Percent CI for ETA1-ETA2 is (-0.0553,0.1083) W = 2392.0 Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at 0.6417 The test is significant at 0.6417 (adjusted for ties)

Mann-Whitney Test and CI: HRC 12 M H, HRC 12 M S N Median HRC 12 M H 42 0.0209 HRC 12 M S 42 -0.0508 Point estimate for ETA1-ETA2 is 0.0667 95.0 Percent CI for ETA1-ETA2 is (-0.0345,0.1602) W = 1932.0 Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at 0.1900

143

Mann-Whitney Test and CI: HRC 18 M H, HRC 18 M S N Median HRC 18 M H 36 0.0245 HRC 18 M S 36 -0.0986 Point estimate for ETA1-ETA2 is 0.0956 95.1 Percent CI for ETA1-ETA2 is (-0.0227,0.2070) W = 1452.0 Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at 0.1215 Mann-Whitney Test and CI: HRC 2 Y H, HRC 2 Y S N Median HRC 2 Y H 30 0.0047 HRC 2 Y S 30 -0.1099 Point estimate for ETA1-ETA2 is 0.1371 95.2 Percent CI for ETA1-ETA2 is (0.0216,0.2959) W = 1074.0 Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at 0.0191

Mann-Whitney Test and CI: HRC 3 Y H, HRC 3 Y S N Median HRC 3 Y H 18 0.0290 HRC 3 Y S 18 -0.1997 Point estimate for ETA1-ETA2 is 0.2687 95.2 Percent CI for ETA1-ETA2 is (0.0819,0.4199) W = 424.0 Test of ETA1 = ETA2 vs ETA1 not = ETA2 is significant at 0.0042

144

APPENDIX D: Transmission Line Projects

Filing

In-Service

Project

Jan-08 Apr-08 Oct-08 Oct-08 Oct-08 Oct-08 Oct-08 Oct-08 Oct-08 Oct-08 Oct-08 Oct-08 Oct-08 Oct-08 Nov-08 Nov-08 Nov-08 Dec-08 Dec-08 Dec-08 Dec-08 Dec-08 Dec-08 Dec-08 Dec-08 Dec-08 Jan-09 Mar-09 Mar-09 Mar-09 Mar-09

Jun-12 Jul-12 Dec-09 Sep-09 Oct-10 Apr-10 Feb-12 May-10 Mar-10 Dec-08 Jul-10 Sep-10 Apr-11 Oct-09 Jun-09 Feb-10 Feb-09 Nov-09 Sep-09 Feb-11 Dec-10 May-09 Jun-11 Nov-10 Dec-09 Dec-11 Oct-09 Dec-09 Oct-09 Jun-10 Apr-09

David Jct. - Bingham 138kV Hybrid Energy Center-Clinch River 138kV Transmission Line Arnold-Vinton-Dysart-Washburn 161kV Reconductor G503 Noble Wind Farm Grand Rapids SAG limits Gray Road Grove Lake - Glenwood line rebuild Hubbardston Road Indiana Arsenal Jct to CMC new 138kV line Keystone - Clearwater - Stover 138 kV line Phase 1 MacSteel GOAB Marathon/Navarre Martinsville to Martinsville SE 69kV Jct Uprate Speed to LGEE Trimble 345kV tie Milroy-Sheridan 69kV line addition Sun Valley T-D Uprate Glenview-Shoto 138 kV Acme Cochran Junction Dyersville West Rebuild Genoa – Latson Hennepin-Oglesby Line 1516 Term. Equipment Reconductor Sioux-Huster-1 and -3 138 kV Triboji-CBPC Milford 69kV Wood Pole Replacement 2009 Wood Pole Replacement 2011 Loop Holland to East Springfield 138 kV line Grand Traverse to East Bay Lake Mills Transmission-Distribution interconnection New Oak Ridge-Verona 138-kV line North Madison-Waunakee 138 kV line 145

Budget (Millions) 11.70 50.00 14.10 7.83 1.00 4.14 16.35 0.17 5.06 17.00 0.20 0.64 0.01 3.20 0.63 3.05 1.41 4.06 0.18 1.06 1.50 2.53 11.10 2.02 4.50 6.00 2.80 3.40 20.45 11.20 14.07

Mar-09 Mar-09 Apr-09 Apr-09 Apr-09 Apr-09 May-09 May-09 May-09 Jun-09 Jun-09 Jun-09 Jun-09 Aug-09 Aug-09 Aug-09 Aug-09 Sep-09 Sep-09 Sep-09 Sep-09 Sep-09 Sep-09 Sep-09 Sep-09 Sep-09 Sep-09 Sep-09 Sep-09 Sep-09 Sep-09 Sep-09 Sep-09 Sep-09 Nov-09 Nov-09 Nov-09 Nov-09 Nov-09 Dec-09

Mar-10 May-09 Dec-10 Sep-10 Apr-12 Oct-12 Dec-09 Nov-11 Feb-10 Feb-11 Jun-11 Apr-10 Oct-11 Dec-11 Jun-11 May-12 May-10 Sep-11 Dec-11 Jun-10 Dec-09 Apr-12 Dec-10 Dec-09 May-12 Dec-09 Dec-10 Oct-10 Jul-12 Jun-10 May-11 Jun-11 Dec-11 Jan-11 Dec-10 Mar-11 Jun-11 May-10 Sep-10 Jan-11

Paddock - Rockdale 345kV Circuit 13823 - Hartsdale to Munster - Capacity Upgrade G595 Network upgrades Mason City Armor - Emery North 69 kV line Monticello-Amber Conversion & Lovell REC Rebuild Wyoming-Massillon 34kV Rebuild G287, 37642-03. Upgrades for G287 G349, 37774-01. Upgrades for G349 Rebuild Arpin-Rocky Run 345 kV Canal Jct - Island Rd 138kV Iosco - East Tawas 138kV State Line - Wolf Lake - Sheffield 138 kV line Tihart - Oakland (Genoa) 138kV Lackawanna Loretto - Piney Grove Sporn Sporn - Waterford Andrew Tap & Monmouth Rebuilds Bellefonte-North Proctorville Bergen Bluefield Avenue - Tazewell Brazil East to Reelsville J 69kV - 6938 ckt. Reconductor Coldwater Decorah Mill St-Cresco dbl ckt Rebuild Frankfort Burlington to Middlefork 69kV - 69133 ckt Uprate Gaylord Gen - Gaylord OCB Goshen Jct. Cir 6976 - Recond 2.1 Miles Haviland - Paulding Huxley Industrial Park Terminal Addition Petersburg - Thompson 345 kV line rating upgrade Powesheik 161kV Breaker Upgrades Rocklane Tie to Duke 69kV West Branch-West Liberty 34kV Rebuild York Badoura Project: Pine River - Pequot Lakes 115 kV line Cannon Falls Transmission Improvements Grand Rapids H-Frame 230 kV Storm Structures Woodland (WH) 1 mile, 115 kV line Blount-Ruskin 69 kV line replacement 146

126.50 0.09 37.00 1.06 3.78 4.02 27.77 26.69 23.54 14.93 9.82 2.56 18.87 0.70 0.50 3.60 27.30 1.87 1.22 45.00 1.00 2.55 2.00 0.20 1.10 1.20 0.19 3.87 0.75 1.50 0.97 3.00 4.15 0.40 17.69 5.50 1.00 0.53 0.15 6.50

Dec-09 Dec-09 Jan-10 Jan-10 Jan-10 Jan-10 Jan-10 Jan-10 Jan-10 Jan-10 Jan-10 Jan-10 Jan-10 Jan-10 Jan-10 Feb-10 Apr-10 Apr-10 Apr-10 Apr-10 Apr-10 Apr-10 Apr-10 Apr-10 Apr-10 Apr-10 Apr-10 Apr-10 Apr-10 Apr-10 May-10 May-10 Jun-10 Jun-10 Jun-10 Jun-10 Jun-10 Jun-10 Jun-10 Jun-10

Oct-10 Jun-10 Jan-11 Dec-10 Aug-10 Jan-11 Jun-10 Jun-10 Apr-11 Feb-10 Jul-10 May-10 Feb-11 Jun-10 Mar-10 Jul-10 May-10 Oct-10 Oct-11 May-12 Jan-12 Dec-10 Dec-11 Apr-11 Jun-12 Dec-12 Nov-10 Jun-10 Jun-11 Aug-11 Apr-12 May-12 Dec-10 Jun-12 Oct-10 Jul-11 Nov-12 Dec-11 Jun-11 Jun-12

N. Farmington-Cape-1 Uprate Bain-Albers 138-kV line Algoma - Croton Construct a new Milton Tap-Milton 69kV line Deacon (formerly Oakwood) East Krok-Kewaunee 138kV line Rebuild Chaffee Creek-Plainfield 69 kV line Rebuild Verona-Oregon 69 kV line Rebuild Whitcomb-Wittenberg 69 kV line Rebuild/uprate Y-207 Sigel-Auburndale-Rozellville 69kV line South Boardman-Kalkaska Generation Line rebuild Uprate Femrite-Royster 69 kV Uprate Forsyth-Munising 138kV Uprate Kaukauna Central Tap-Meadows Tap-Melissa 138kV Wick Rd T-D Interconnection Uprate Perch Lake-M38 138kV AECI Spalding Substation Connection Badoura - Birch Lake 115 lines Big River-Rockwood 138 kV Brokaw-State Farm Line 1596 - Reconductor Edenville Jct. - Warren 138kV Grafton 41.6 kV Line Upgrade Grand Tower-Steeleville 138 kV Reconductor Greentown to Peru SE 23021 uprate to 100C New Northeast to Oak Grove to Culley Line 138 kV New Transmission Line Gibson to AB Brown to Reid Orr (LCP) 13 mile, 69 kV line Pilot Knob--Yankee Doodle Projects Stallings-E. Collinsville Terminal Equipment upgrade at Tippy Sandy Creek-Joachim Reconductoring Wheatland - Bloomington 345 kV line 6th Street - Beverly Aquia Harbour - Garrisonville 230kV Double-Circuit Line G870-Freeborn H007-Bond Breaker Station LaSalle Area Development LaSalle Area Development Midway-N. Staunton Reconductoring Wood River-Stallings 147

0.42 0.12 3.30 2.97 0.20 1.25 4.96 6.10 3.68 2.08 1.10 0.44 0.13 0.85 4.75 0.22 0.75 11.25 13.38 2.57 12.50 0.06 1.87 0.00 16.41 103.79 6.86 1.77 0.74 0.06 3.31 105.00 7.20 120.00 3.52 3.56 8.96 21.36 4.91 2.04

Jul-10 Jul-10 Sep-10 Sep-10 Sep-10 Sep-10 Sep-10 Sep-10 Sep-10 Sep-10 Sep-10 Sep-10 Sep-10 Sep-10 Sep-10 Sep-10 Sep-10 Sep-10 Sep-10 Oct-10 Oct-10 Oct-10 Oct-10 Jan-11 Jan-11 Jan-11 Jan-11 Feb-11 Feb-11 Feb-11 Feb-11 Feb-11 Mar-11 Apr-11 Apr-11 Jun-11 Jul-11 Sep-11 Sep-11 Sep-11

Dec-12 Dec-11 Aug-12 Dec-10 Jul-12 May-11 Dec-10 Dec-11 Oct-10 Jun-12 Jan-12 Jun-12 Jun-11 Sep-12 Jul-11 Nov-10 Apr-11 Nov-10 Apr-11 Feb-11 Mar-12 Mar-11 Apr-11 Apr-11 Dec-11 Jun-11 Feb-11 Jun-11 Oct-12 Jun-12 Dec-12 Jun-12 Feb-12 Dec-11 Dec-12 Jan-12 May-12 Jul-12 Jan-12 Aug-12

G359, 38073-01 Lakefield-Adams 161kV Rebuild Blomkest (KEPCA) 3.0 mile, 69 kV line Bluff Point - Randolph Burnips to Wayland Byron Station 6 - Wayne 144 Circuits 13874-Pole Replacement Delaware - Centerville East Lima - Marysville Eau Claire - Madison Street Rebuild Elnora to Newberry 69kV Line Rebuild Hudson - Essex Line Y-95 Fount Valley-Fountain Valley Marysville - Southwest Lima Millbrook Park - South Portsmouth Olive - Dequine U1-060 - Sterling Uprate Blue River Tap-Muscoda 69 kV Uprate Straits-McGulpin 138 kV H.E. Rocklane Alternate 69kV Feed Inland Container to Hillsdale 69kV Line Rebuild Nordic-Perch Lk Uprate (AM) Stage Coach-Timberlane 69kV uprate A-157 Hume-Wildwood 115kV Uprate Bradley TSS 70 Louise 115 kV Interconnection N-144 McMillan-Wildwood 115kV Uprate CBEC-River Bend 161 kV Rebuild City of Redwood Falls, MN load serving upgrades City of St Peter, MN load serving upgrades G517: Dotson - Storden - Heron Lake 161kV Oak Grove Gaylord to Advance to Petoskey to Oden 69 kV line rebuild Katydid Road TSS196 Vinburn T-D Interconnection Danville to Danville Jct 69kV reconductor Dahlgren 230kV Transmission Line Project 25 Line tap Alba to Advance 69 rebuild East Lima - Marysville 148

16.57 97.58 1.76 0.60 9.02 48.00 0.60 0.30 0.64 1.60 1.56 50.50 0.36 0.64 14.30 0.60 17.90 0.58 0.30 0.06 3.66 1.54 0.04 0.05 0.10 6.20 0.05 0.47 4.00 6.00 36.72 14.00 13.40 2.98 3.36 2.21 36.40 1.29 6.18 0.60

Sep-11 Sep-11 Sep-11 Sep-11 Sep-11 Sep-11 Sep-11 Oct-11 Nov-11 Dec-11 Jan-12 Jan-12 Jan-12 Jan-12 Jan-12 Jan-12 Jan-12 Feb-12 Mar-12 Mar-12 Apr-12 Apr-12 May-12 Sep-12 Sep-12 Oct-12 Oct-12 Nov-12 Nov-12 Dec-12 Dec-12

Dec-11 Dec-12 Dec-11 Dec-12 Apr-12 Nov-12 Dec-12 Dec-11 May-12 Jun-12 Jun-12 Sep-12 May-12 Feb-12 Mar-12 Aug-12 Nov-12 May-12 May-12 Jun-12 Sep-12 Oct-12 Nov-12 Oct-12 Dec-12 Dec-12 Nov-12 Dec-12 Jan-13 Mar-13 Jan-13

Hillsdale - Clinton Lilly 69kV Rebuild Phase 3 Keystone - Sorenson Newberry - Bloomfield 69kV Rebuild Phase 3 Raise tower height on ETCO-Forbes 115 kV line Warwick to Devils Lake East Pump Station 41.6kV Line Westwood to W. Lafayette 138kV line uprate Wood Pole Replacement Program Oakes - Forman 230 kV Line Rebuild Church Lore-Seippel 69 kV Rebuild Clear Lake-Woodmin 115 kV Engadine Load Move Rebuild Dane-Okee 69 kV Rebuild Sunset Point-Pearl Ave 69 kV line Truro Line Rebuild Uprate Oak Creek-Bluemound 230 kV Uprate X-43 Saratoga-Badger West-Petenwell 138 kV, Phase 2 Hollymead Transmission Line Dooms - Bremo Rebuild Project Freeborn-Hayward 161kV Rebuild HE Salisbury - Georgetown 69kV rebuild Newberry - Bloomfield 69kV Rebuild Phase 3 Kansas, West-Hutsonville 138 kV Line Rebuild Green Valley 41.6 kV Line Upgrade Uprate Nine Springs Pflaum Area 69 kV Desoto Dunkirk-East Lima Desoto-Tanners Creek Madison-Tanners Creek 138kV N. Meshoppen to X2-023 E. Towanda - E. Sayreville

149

1.93 1.00 3.08 0.40 1.50 0.00 6.00 3.20 2.00 0.94 12.88 0.59 7.35 3.31 3.36 1.41 1.09 34.40 64.00 0.00 1.67 3.08 22.59 0.25 4.41 0.18 0.38 0.60 0.41 4.08 0.13