Handbook of

Mulö-Commodity

Mapkets and Products

Structuring, Trading and Risk Management

Edited by

ANDREA RONCORONI

GIANLUCA FUSAI

MARK CUMMINS

WlLEY

Contents

Pretace xix

flcknowledgements xxiii

Almut the Editors xxv

List ol Contributors xxvii

PART ONE Commodity Markets and Products

CHAPTER1 Dil Markets and Products 3 Cristiano Campi and Francesco Galdenzi 1.1 Introduction 3 1.2 Risk Management for Corporations: Hedging Using Derivative Instruments 4

1.2.1 Crude Oil and Oil Products Risk Management for Corporations 4 1.2.2 Aviation: Risk Profile and Hedging Strategie« 11 1.2.3 Shipping: Risk Profile and Hedging Strategies 20 1.2.4 Land Transportation: Risk Profile and Hedging Strategies 27 1.2.5 Utilities: Risk Profile and Hedging Strategies 32 1.2.6 Refineries: Risk Profile and Hedging Strategies 35 1.2.7 Industrial Consumers: Risk Profile and Hedging Strategies 40

1.3 Oil Physical Market Hedging and Trading 41 1.3.1 The Actors, Futures and OTC Prices 41 1.3.2 The Most Commonly Used Financial Instruments 45 1.3.3 How to Monitor and Manage Risk 49 1.3.4 How to Create a Market View 52 1.3.5 Trading Strategies to Maximize a Market View 54 Further Reading 66

vii

viii CONTENTS

CHARTER 2 Goal Markets and Products Lars Schernikau 2.1 Introduction 2.2 Source of Goal - Synopsis of the Resource Coal

2.2.1 The Fundamentals of Energy Sources and Fossil Fuels 2.2.2 Process of Coal Formation 2.2.3 Coal Classification 2.2.4 Reserves and Resources 2.2.5 Coal Mining and Production

2.3 Use of Coal - Power Generation and More 2.3.1 Steam Coal and its Role in Power Generation 2.3.2 Coal-Fired Power Plant Technologies 2.3.3 Cement and Other Industry 2.3.4 Alternatives to Coal: Shale Gas and Other 2.3.5 Future Trend: CtL and Coal Bed Methane

2.4 Overview of Worldwide Steam Coal Supply and Demand 2.4.1 Atlantic Demand Market: Europe at its Core 2.4.2 Pacific Demand Market: China, India, Japan, Taiwan, Korea and SEA 2.4.3 Steam Coal Supply Regions: ID, AU, USA, SA, RU, CO and Others 2.4.4 Seaborne Freight 2.4.5 Geopolitical and Policy Environment

2.5 The Global Steam Coal Trade Market and its Future 2.5.1 Current and Future Market Dynamics of the Coal Trade 2.5.2 Future Steam Coal Price Trends 2.5.3 Future Source of Energy: What Role Will Coal Play?

2.6 Concluding Words Abbreviations and Definitions Acknowledgements References

CHARTERS Natural Gas Markets and Products Mark Cummins and Bernard Murphy 3.1 Physical Natural Gas Markets

3.1.1 Physical Structure 3.1.2 Natural Gas Market Hubs and Main Participants 3.1.3 Liquefied Natural Gas 3.1.4 Shale Gas

3.2 Natural Gas Contracting and Pricing 3.2.1 Natural Gas Price Formation

3.3 Financial Natural Gas Markets 3.3.1 Exchange-Based Markets 3.3.2 Natural Gas Futures 3.3.3 Natural Gas Options 3.3.4 OTC Markets and Products References

67

67 72 72 74 74 79 83 90 91 93 95 95

101 102 102 104 107 116 118 121 121 125 127 129 130 132 132

135

135 141 146 147 149 154 155 158 158 159 172 179 180

Contents ix

CHARTER 4 Electricity Markets and Products 181 Stefano Fiorenzani, Bernard Murphy and Mark Cummins 4.1 Market Structure and Price Components 181

4.1.1 Spot and Forward Markets 181 4.1.2 Supply and Demand Interaction 183 4.1.3 Electricity Derivatives 186 4.1.4 Power Price Models 189 4.1.5 Spot Price Analysis (IPEX Case) 196 4.1.6 Forward Price Analysis (EEX Case) 200

4.2 Renewables, Intra-Day Trading and Capacity Markets 205 4.2.1 Renewables Expansion Targets 205 4.2.2 Growth in Intra-Day Trading 206 4.2.3 Implications for Future Price Volatility and Price Profiles 207 4.2.4 Reforms and Innovations in Capacity Markets 209 4.2.5 Provision and Remuneration of Flexibility - Storage Assets 212

4.3 Risk Measures for Power Portfolios 216 4.3.1 Value-B ased Risk Measures 216 4.3.2 Flow-Based Risk Measures 218 4.3.3 Credit Risk for Power Portfolios 220 References 221 Further Reading 221

CHAPTER 5 Emissions Markets and Products 223 Marc Chesney, Luca Taschini and Jonathan Gheyssens 5.1 Introduction 223 5.2 Climate Change and the Economics of Externalities 224

5.2.1 The Climate Change Issue 224 5.2.2 The Economics of Externality and GHG Pollution 226

5.3 The Kyoto Protocol 227 5.3.1 The United Nations Framework Convention on

Climate Change 227 5.3.2 The Conference of Parties and the Subsidiary Bodies 229 5.3.3 The Kyoto Protocol 229 5.3.4 The Road to Paris 231

5.4 The EU ETS 232 5.4.1 Institutional Features 232 5.4.2 Allocation Criteria 234 5.4.3 Market Players and the Permit Markets 236 5.4.4 The Future of the EU ETS 238

5.5 Regional Markets: A Fragmented Landscape 239 5.5.1 Regional Markets 239 5.5.2 Voluntary Markets 240

5.6 A New Asset Class: C02 Emission Permits 241 5.6.1 Macroeconomic Models 242 5.6.2 Econometric Investigation of C02 Permit Price Time-Series 243

X CONTENTS

5.6.3 Stochastic Equilibrium Models 251 Abbreviations 252 References 252

CHARTERS Weather Risk and Weather Derivatives 255 Alessandro Mauro 6.1 Introduction 255 6.2 Identification of Volumetrie Risk 257

6.2.1 Weather Events on the Demand Curve 258 6.2.2 Weather Events on the Supply Curve 260 6.2.3 Risk Measurement and Weather-at-Risk 262

6.3 Atmospheric Temperature and Natural Gas Market 264 6.3.1 Characterization of the Air Temperature Meteorological Variable 264 6.3.2 Degree Days 267 6.3.3 Volumetrie Risk in the Natural Gas Market 270

6.4 Modification of Weather Risk Exposure with Weather Derivatives 272 6.4.1 Weather Derivatives for Temperature-Related Risk 273

6.5 Conclusions 276 Nomenclature 277 References 277

CHARTER? Industrie! Metals Markets and Products 279 Alessandro Porru 7.1 General Overview 279

7.1.1 Brief History of the LME 280 7.1.2 Non-ferrous Metals 282 7.1.3 Other Metals 291 7.1.4 LME Instruments 292 7.1.5 OTC Instruments 298 7.1.6 A New Player: The Investor 301

7.2 Forward Curves 305 7.2.1 Building LME's Curves in Practice 308 7.2.2 Interpolation 3 \ 3 7.2.3 LME, COMEX and SHFE Copper Curve and Arbitrage 314 7.2.4 Contango Limit... 31g 7.2.5 ... and No-Limit Backwardation 324 7.2.6 Hedging the Curve in Practice 328

7.3 Volatility 337 7.3.1 A European Disguised as an American 33g 7.3.2 LME's Closing Volatilities 339 7.3.3 Sticky Strike, Sticky Delta and Skew 342 7.3.4 Building the Surface in Practice 345 7.3.5 Considerations on Vega Hedging 34g Acknowledgements 352

References ^53 Further Reading w

Contents xi

CHARTER 8 Freight Markets and Products 355 Manolis G. Kavussanos, Ilias D. Visvikis and Dimitris N. Dimitrakopoulos 8.1 Introduction 355 8.2 Business Risks in Shipping 356

8.2.1 The Sources of Risk in the Shipping Industry 356 8.2.2 Market Segmentation in the Shipping Industry 358 8.2.3 Empirical Regularities in Freight Rate Markets 359 8.2.4 Traditional Risk Management Strategies 365

8.3 Freight Rate Derivatives 366 8.3.1 Risk Management in Shipping 366 8.3.2 The Underlying Indices of Freight Rate Derivatives 366 8.3.3 The Freight Derivatives Market 372 8.3.4 Examples of Freight Derivatives Trading 380

8.4 Pricing, Hedging and Freight Rate Risk Measurement 382 8.4.1 Pricing and Hedging Effectiveness of Freight Derivatives 382 8.4.2 Value-at-Risk (VaR) in Freight Markets 384 8.4.3 Expected Shortfall (ES) in Freight Markets 389 8.4.4 Empirical Evidence on Freight Derivatives 390

8.5 Other Derivatives for the Shipping Industry 393 8.5.1 Bunker Fuel Derivatives 393 8.5.2 Vessel Value Derivatives 395 8.5.3 Foreign Exchange Rate Derivatives Contracts 395 8.5.4 Interest Rate Derivatives Contracts 396

8.6 Conclusion 396 Acknowledgements 396 References 397

CHARTERS Agricultural and Soft Markets 398 Francis Declerk 9.1 Introduction: Stakes and Objectives 399

9.1.1 Stakes 399 9.1.2 Objectives 399

9.2 Agricultural Commodity Specificity and Futures Markets 400 9.2.1 Agricultural Commodity Specificity 400 9.2.2 Volatility of Agricultural Markets 402 9.2.3 Forward Contract and Futures Contract 402 9.2.4 Major Agricultural Futures Markets and Contracts 404 9.2.5 Roles of Futures Markets 405 9.2.6 Institutions Related to Futures Markets 406 9.2.7 Commodity Futures Contracts 406 9.2.8 The Operators 408 9.2.9 Monitoring Hedging: Settlement 409 9.2.10 Accounting and Tax Rules 409

9.3 Demand and Supply, Price Determinants and Dynamics 409 9.3.1 Supply and Demand for Agricultural Commodities:

The Determinants 409

xii CONTENTS

9.3.2 Agricultural Market Prices, Failures and Policies 413 9.3.3 The Price Dynamics of Seasonal and Storable Agricultural

Commodities 416 9.3.4 The Features of Major Agricultural and Soft Markets 417

9.4 Hedging and Basis Management 466 9.4.1 Short Hedging for Producers 466 9.4.2 Long Hedging for Processors 469 9.4.3 Management of Basis Risk 471

9.5 The Financialization of Agricultural Markets and Hunger: Speculation and Regulation 480 9.5.1 Factors Affecting the Volatility of Agricultural Commodity Prices 480 9.5.2 Financialization: Impact of Non-commercial Traders on

Market Price 483 9.5.3 The Financialization of Grain Markets and Speculation 484 9.5.4 Bubble or Not, Agricultural Commodities have Become an

Asset Class 489 9.5.5 Price Volatility and Regulation 490 9.5.6 Ongoing Research about Speculation and Regulation 493

9.6 Conclusion about Hedging and Futures Contracts 493 9.6.1 Hedging Process 493 9.6.2 Key Success Factors for Agricultural Commodity

Futures Contracts 494 9.6.3 Conclusion and Prospects 495 References 495 Further Reading 495 Glossary, Quotations and Policy on Websites 497

CHARTER 10 Foreign Exchange Markets and Products 49g Antonio Castagna 10.1 The FX Market 499

10.1.1 FX Rates and Spot Contracts 499 10.1.2 Outright and FX Swap Contracts 500 10.1.3 FX Option Contracts 504 10.1.4 Main Traded FX Options Structures 507 Pricing Models for FX Options 509

10.2.1 The Black-Scholes Model The Volatility Surface

10.4 Barrier Options 10.4.1 A Taxonomy of Barrier Options c, 2 Sources of FX Risk Exposure 5 j 3

m ?imS w c XP°SUreS Embedded in Energy and Commodity Contracts 517 10.6.1 FX Forward Exposures and Conversions 51« 10.6.2 FX-Linked Energy Contracts 522 Typical Hedging Structures for FX Risk Exposure < 71 10.7.1 Collar Piain Vanilla 10.7.2 Leveraged Forward }

536

10.2

10.3

10.5 10.6

10.7

510 511 512

Contents xiii

10.7.3 Participating Forward 10.7.4 Knock-Out Forward 10.7.5 Knock-In Forward 10.7.6 Knock-In Knock-out Forward 10.7.7 Resettable Forward 10.7.8 Range Resettable Forward References

PART TW0 Quantitative Topics

CHARTER 11 An Introduction to Stochastic Calculus with Matlab® Examples Laura Ballotta and Gianluca Fusai 11.1 Brownian Motion

11.1.1 Defining Brownian Motion 11.2 The Stochastic Integral and Stochastic Differential Equations

11.2.1 Introduction 11.2.2 Defining the Stochastic Integral 11.2.3 The Itö Stochastic Integral as a Mean Square Limit of Suitable

Riemann-Stieltjes Sums 11.2.4 A Motivating Example: Computing W(s)dW(s) 11.2.5 Properties of the Stochastic Integral 11.2.6 Itö Process and Stochastic Differential Equations 11.2.7 Solving Stochastic Integrals and/or Stochastic Differential

Equations 11.3 Introducing Itö' s Formula

11.3.1 A Fact from Ordinary Calculus 11.3.2 Itö's Formula when Y = g(x), g(x) e C2

11.3.3 Guiding Piinciple 11.3.4 Itö's Formula when Y(t) = g(t,X), g(t,X) e C1'2

11.3.5 The Multivariate Itö's Lemma when Z — g(t,X, Y) 11.4 Important SDEs

11.4.1 The Geometrie Brownian Motion GBM(j/, a) 11.4.2 The Vasicek Mean-Reverting Process 11.4.3 The Cox-Ingersoll-Ross (CIR) Model 11.44 The Constant Elasticity of Variance (CEV) Model 11.4.5 The Brownian Bridge 11.4.6 The Stochastic Volatility Heston Model (1987)

11.5 Stochastic Processes with Jumps 11.5.1 Preliminaries 11.5.2 Jump Diffusion Processes 11.5.3 Time-Changed Brownian Motion 11.5.4 Final Remark: Levy Processes References Further Reading

538 540 543 545 548 550 553

557

558 558 566 566 567

567 568 569 571

573 575 576 576 577 577 578 581 581 588 595 604 607 611 618 618 623 628 632 633 633

CHARTER 12 Estimating Commodity Term Structure Volatilities Andrea Roncoroni, Rachid Id Brik and Mark Cummins 12.1 Introduction 12.2 Model Estimation Using the Kaiman Filter

12.2.1 Description of the Methodology 12.2.2 Case Study: Estimating Parameters on Crude Oil

12.3 Principal Components Analysis 12.3.1 PCA: Technical Presentation 12.3.2 Case Study: Risk Analysis on Energy Markets

12.4 Conclusion Appendix References

CHARTER 13 Nonparametric Estimation of Energy and Commodity Price Processes Gianna Figä-Talamanca and Andrea Roncoroni 13.1 Introduction 13.2 Estimation Method 13.3 Empirical Results

References

CHARTER 14 How to Build Electricity Forward Curves Ruggero Caldana, Gianluca Fusai and Andrea Roncoroni 14.1 Introduction 14.2 Review of the Literature 14.3 Electricity Forward Contracts 14.4 Smoothing Forward Price Curves 14.5 An Illustrative Example: Daily Forward Curve 14.6 Conclusion

References

CHARTER 15 GARCH Models for Commodity Markets Eduardo Rossi and Filippo Spazzini 15.1 Introduction 15.2 The GARCH Model: General Definition

15.2.1 The ARCH(g) Model 15.2.2 The GARCH(p,ij) Model 15.2.3 The Yule-Walker Equations for the Squared Process 15.2.4 Stationarity of the GARCH(p,g) 15.2.5 Porecasting Volatility with GARCH

15.3 The IGARCH(p,g) Model 15.4 A Permanent and Transitory Component Model of Volatility 15.5 Asymmetrie Models

15.5.1 The EGARCH(p,q) Model

Contents XV

15.5.2 Other Asymmetrie Models 704 15.5.3 The News Impact Curve 706

15.6 Periodic GARCH 707 15.6.1 Periodic EGARCH 708

15.7 Nesting Models 708 15.8 Long-Memory GARCH Models 713

15.8.1 The FIGARCH Model 716 15.8.2 The FIEGARCH Model 719

15.9 Estimation 720 15.9.1 Likelihood Computation 720

15.10 Inference 722 15.10.1 Testing for ARCH Effects 722 15.10.2 Test for Asymmetrie Effects 723

15.11 Multivariate GARCH 725 15.11.1 BEKK Parameterization of MGARCH 726 15.11.2 The Dynamic Conditional Correlation Model 726

15.12 Empirical Applications 727 15.12.1 Univariate Volatility Modelling 727 15.12.2 A Simple Risk Measurement Application: A Bivariate Example

with Copulas 733 15.13 Software 740

References 748

CHARTER IG Pricing Commodity Swaps with Counterparty Credit Risk: The Case of Credit VaiueAdjustment 755 Marina Marena, Gianluca Fusai and Chiara Quaglini 16.1 Introduction 755

16.1.1 Energy Company Strategies in Derivative Instruments 755 16.2 Company Energy Policy 756

16.2.1 Commodity Risk 756 16.2.2 Credit Risk 757

16.3 A Focus on Commodity Swap Contracts 758 16.3.1 Definition and Main Features of a Commodity Swap 758

16.4 Modelling the Dynamics of Oil Spot Prices and the Forward Curve 760 16.4.1 The Schwartz and Smith Pricing Model 760

16.5 An Empirical Application 764 16.5.1 The Commodity Swap Features 764 16.5.2 Calibration of the Theoretical Schwartz and Smith

Forward Curve 765 16.5.3 The Monte Carlo Simulation of Oil Spot Prices 772 16.5.4 The Computation of Brent Forward Curves at Any Given

Valuation Date 773 16.6 Measuring Counterparty Risk 777

16.6.1 CVA Calculation 779

xvi CONTENTS

16.6.2 Swap Fixed Price Adjustment for Counterparty Risk 782 16.6.3 Right-and Wrong-Way Risk 784

16.7 Sensitivity Analysis 788 16.8 Accounting for Derivatives and Credit Value Adjustments 788

16.8.1 Example of Hedge Effectiveness 791 16.8.2 Accounting for CVA 796

16.9 Conclusions 797 References 798 Further Reading 798

CHARTER 17 Pnicing Energy Spread Options 801 Fred Espen Benth and Hanna Zdanowicz 17.1 Spread Options in Energy Markets 801 17.2 Pricing of Spread Options with Zern Strike 805 17.3 Issues of hedging 813 17.4 Pricing of Spread Options with Nonzero Strike 815

17.4.1 Kirk's Approximation Formula 817 17.4.2 Approximation by Margrabe Based on Taylor Expansion 820 17.4.3 Other Pricing Methods 823 Acknowledgement 824 References 825

CHARTER 18 Asian Options: Payoffs and Pricing Models 827 Gianluca Fusai, Marina Marena and Giovanni Longo 18.1 Payoff Structures 832 18.2 Pricing Asian Options in the Lognormal Setting 833

18.2.1 Moment Malching 835 18.2.2 Lower Price Bound 844 18.2.3 Monte carlo Simulation 845

18.3 A Comparison 355 18.4 The Flexible Square-Root Model 858

18.4.1 General Setup 861 18.4.2 Numerical Results 870 18.4.3 A Case Study g7j

18.5 Conclusions g74 References g74

CHARTER 19 Natural Gas Storage Modelling 877

Älvaro Cartea, James Cheeseman and Sebastian Jaimungal 19.1 Introduction 19.2 A Simple Model of Storage, Futures Prices, Spot Prices And

Convenience Yield 070 19.3 Valuation of Gas Storage 88Q

19.3.1 Least-Squares Monte Carlo oS1

Contents XVÜ

19.3.2 LSMC Greeks 883 19.3.3 Extending the LSMC to Price Gas Storage 883 19.3.4 Toy Storage Model 884 19.3.5 Storage LSMC 888 19.3.6 Swing Options 890 19.3.7 Closed-Form Storage Solution 891 19.3.8 Monte Carlo Convergence 892 19.3.9 Simulated Storage Operations 894 19.3.10 Storage Value 897 References 899

CHARTER 20 Commodity-Linked Arbitrage Strategies and Portfolio Management 901 Viviana Fanelli 20.1 Commodity-Linked Arbitrage Strategies 902

20.1.1 The Efficient Market Hypothesis 902 20.1.2 Risk Arbitrage Opportunities in Commodity Markets 903 20.1.3 Basic Quantitative Trading Strategies 906 20.1.4 A General Statistical Arbitrage Trading Methodology 914

20.2 Portfolio Optimization with Commodities 921 20.2.1 Commodities as an Asset Class 921 20.2.2 Commodity Futures Return Characteristics 923 20.2.3 Risk Premiums in Commodity Markets 925 20.2.4 Commodities as a Portfolio Diversifier 928 20.2.5 Risk-Return Optimization in Commodity Portfolios 929 Symbols 936 References 936

CHARTER 21 Econometric Analysis of Energy and Commodity Markets: Multiple Hypothesis Testing Tecluiiques Mark Cummins 21.1 Introduction 21.2 Multiple Hypothesis Testing

21.2.1 Generalized Familywise Error Rate 21.2.2 Per-Familywise Error Rate 21.2.3 False Discovery Proportion 21.2.4 False Discovery Rate 21.2.5 Single-Step and Stepwise Procedures

21.3 Energy-Emissions Market Interactions 21.3.1 Literature Review 21.3.2 Data Description 21.3.3 Testing Framework 21.3.4 Empirical Results

21.4 Emissions Market Interactions 21.4.1 Testing Framework and Data 21.4.2 Empirical Results

939

939 940 941 942 942 943 943 943 943 944 945 950 953 953 955

xviii CONTENTS

21.5 Quantitative Spread Trading in Oil Markets 956 21.5.1 Testing Framework and Data 956 21.5.2 Optimal Statistical Arbitrage Model 957 21.5.3 Resampling-Based MHT Procedures 959 21.5.4 Empirical Results 964 References 964

APPENDIX A Quick Review of Distributions Relevant in Finance with Matlab® Examples 967 Laura Ballotta and Gianluca Fusai

Index 1005