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©Copyright 2016 Thomas Fillebeen How Do Small Players Deduce Beliefs About Uncertainty? a Look at Texas Shale Oil Investments

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©Copyright 2016 Thomas Fillebeen How Do Small Players Deduce Beliefs About Uncertainty? a Look at Texas Shale Oil Investments ©Copyright 2016 Thomas Fillebeen How Do Small Players Deduce Beliefs about Uncertainty? A Look at Texas Shale Oil Investments Thomas Fillebeen A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Washington 2016 Reading Committee: Chris Anderson, Chair Robert Halvorsen, Chair Eric Zivot Program Authorized to Offer Degree: Economics University of Washington Abstract How Do Small Players Deduce Beliefs about Uncertainty? A Look at Texas Shale Oil Investments Thomas Fillebeen Co-Chairs of the Supervisory Committee: Associate Professor Chris Anderson School of Aquatic and Fishery Sciences Professor Robert Halvorsen Department of Economics Derivative markets enable firms to eliminate unwanted risk and, thereby, focus on their core competence. We ask whether small firms respond to changes in complex risk struc- tures, using the emerging Texas shale oil industry. Unlike their conventional drilling coun- terparts, shale oil producers are small, with short production lead times and most revenue earned in two to three years. This exposes them to recent unprecedented volatility in product prices, production costs, and-in the Permian, pipeline transportation basis risk. We develop a real option model of their decision process, within which we measure firm responsiveness to volatility. To estimate revenue expectations, we use forward-looking time-varying beliefs deduced from futures and futures option prices. For costs, we use expectations about the evolution of production technology and forward-contracted trans- port capacity. Our results show that investment in new wells responds optimally not only to time-varying market price, but also to a second source of uncertainty: transportation basis. This demonstrates that even small firms are able to coordinate their production activity to integrate two sources of risk, impacting their investment returns. Lastly, we estimate the impact of the Nixon-era crude export ban, which became binding in August 2013, and caused US domestic crude price to fall below the world price, distorting US shale oil production revenue by $10.7B. TABLE OF CONTENTS Page List of Figures..................................... iii List of Tables......................................v Chapter 1: Managerial decision-making......................1 1.1 Shale Oil Environment............................4 1.2 Energy Policy Implications..........................7 Chapter 2: Price or Market Risk.......................... 12 2.1 Shale Oil Production............................. 12 2.2 Data and Methods.............................. 16 2.3 Model Empirical Fit............................. 28 2.4 Results and Discussion............................ 32 Chapter 3: Transportation Basis Risk....................... 35 3.1 Transportation Basis............................. 35 3.2 Revenue Integrating Transportation Basis and Volatility......... 36 3.3 Permian Firm Complex Volatility Response................ 40 3.4 Conclusion................................... 42 Chapter 4: Energy Policy: Cost of the Crude Export Ban............ 45 4.1 Unconventional Oil Response in Texas................... 45 4.2 Counterfactual and Policy Valuation Data................. 45 4.3 Real Option Model and Previous Results.................. 47 4.4 Investing Under the Export Ban....................... 51 4.5 Conclusion................................... 64 Bibliography...................................... 67 Appendix A: TECHNICAL DETAILS & DATA, CHAPTER 2.......... 71 A.1 Well Investment in the Permian and Eagle Ford.............. 71 i A.2 Firm Volatility Behavior Identification................... 73 A.3 State Transition Process: Future Expected Evolution........... 74 A.4 Implied Volatility Term Structure...................... 74 A.5 Total Cost per Well.............................. 82 A.6 Descriptive Decision-Making......................... 88 A.7 Real Option Model Intuition......................... 90 A.8 Log-Likelihood................................ 92 A.9 Diffusion Process Drift Term......................... 95 A.10 Dynamic Programming Algorithm Depiction................ 95 Appendix B: TECHNICAL DETAILS, CHAPTER 3............... 97 B.1 Transportation Basis............................. 97 B.2 Permian Graphical Fitting.......................... 104 Appendix C: TECHNICAL DETAILS, CHAPTER 4............... 106 C.1 Medium-Run Global Supply......................... 106 C.2 Counterfactual Cost Adjustment....................... 107 C.3 Ineffective Export Ban as of Dec 2014.................... 108 ii LIST OF FIGURES Figure Number Page 2.1 Drilling Trigger................................ 15 2.2 Eagle Ford Higher Production Growth than Permian........... 16 2.3 Texas Primary Shale Oil Basins....................... 18 2.4 Eagle Ford & Permian Decline Rate per Well................ 21 2.5 Futures Data & Implied Volatility Estimates................ 22 2.6 Eagle Ford Myopic β = 0 & Time-Varying β = 1:08............ 33 3.1 Basis Adjusted 24-Month Permian Price.................. 39 4.1 US Crude Oil and Product Pipeline Map.................. 51 4.2 PADD Petroleum Products Imports..................... 53 4.3 Benchmark Crude Oils............................ 54 4.4 Crude Front and 39-Month Futures Prices................. 55 4.5 Futures Price Differential........................... 57 4.6 Identifying the Binding Export Ban..................... 58 4.7 Well Three Year Total Production...................... 60 4.8 Eagle Ford Real Option Results....................... 61 4.9 Permian Real Option Results........................ 63 A.1 Number of Onshore Oil Wells: Conventional and Shale Oil........ 71 A.2 Number of Onshore Oil Wells: Shale Oil.................. 72 A.3 WTI Fixed Effect Implied Volatility Term Structure............ 75 A.4 Forward Curve Volatility Functions..................... 78 A.5 Crude Oil Implied Volatility Surface for September 2014......... 79 A.6 Crude Oil Parametric and Non-Parametric Volatility Ratio........ 81 A.7 Eagle Ford and Permian Total Cost..................... 83 A.8 Drilling Cost and Price............................ 85 A.9 Day Count Adjusted Drilling Cost and Price................ 85 A.10 First Month Productivity (bbl) per Well.................. 87 A.11 Eagle Ford Cost Adjusted by Productivity per Well............ 87 A.12 Permian Cost Adjusted by Productivity per Well............. 88 iii A.13 Monthly Well Count, Futures Price & Implied Volatility......... 91 A.14 Drilling Expected Payoff........................... 92 A.15 Outer(Parameter Values)/Inner(Structural and LL) Loop......... 96 A.16 Price, Cost, and Volatility Transition Array................ 96 B.1 Eagle Ford and Permian Spot Transportation Basis............ 97 B.2 One Month Ahead Price and Transportation Risk Identification..... 98 B.3 30-Month Forward Simulation........................ 99 B.4 30-Month Dual Risk Simulation....................... 99 B.5 Expected Transportation Basis....................... 103 B.6 Permian Pipeline Projects.......................... 103 B.7 Permian Myopic β = 0 & Time-Varying β = 1:04............. 105 C.1 Small Shale Oil 2014 Production & Breakeven............... 106 C.2 Rig Cost with and without the Export Ban................. 108 C.3 Price Level with and without the Export Ban............... 109 iv LIST OF TABLES Table Number Page 2.1 Real Option Results for Eagle Ford..................... 32 3.1 Real Option Results for Permian...................... 41 4.1 Permian and Eagle Ford Model Results................... 51 A.1 Market Concentration: Wells Drilled and Completed........... 73 A.2 Hazard Function: Eagle Ford Likelihood of Drilling & Completion.... 91 v ACKNOWLEDGMENTS I'd like to express my sincere appreciation to the University of Washington, where I had the opportunity to learn from Professors Chris M. Anderson and Robert F. Halvorsen, whose guidance, supervision, and accessibility were invaluable. The numerous Anderson lab meetings throughout the years, and their participants, served as incubators for my research. Also, I'm grateful to Professor Levis A. Kochin, who helped shape and steer the topic through multiple long conversations, and to Professor Eric W. Zivot for his help selecting the methods and supporting the project early on and throughout. Lastly, I'd like to thank Professor Joe P. Mahoney for sharing his energy infrastructure knowledge. I am appreciative of suggestions from participants of numerous seminars including, but not limited to, Ibrahim Demir, Anthony Sanford, Alexander Rodivilov, and Jorge Rojas. I thank Danyaal Khan and Phong Tran for their technical expertise in managing the data. vi DEDICATION to my lovely wife, parents and brother vii 1 Chapter 1 MANAGERIAL DECISION-MAKING It is unclear how small firms—independent price-takers with short lead-times and pro- duction horizons|deduce beliefs about uncertainty when making irreversible investment decisions. As uncertainty increases, real options theory stipulates that the opportunity cost of postponing an investment decreases. This thesis argues that it is the integra- tion of this time-dependent option to delay, which can incorporate multiple sources of uncertainty, that shows firm sophistication in estimating risk. We value a small1 firm’s investment project using a real option model and test whether its investment heuristics mimic those of an optimal decision-making process by measuring whether myopic con- stant or market time-varying uncertainty yields a better fit of firm investment behavior. Price and price volatility expectations
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