Andrea Roncoroni, Gianluca Fusai, Mark Cummins

Handbook of Multi-Commodity Markets and Products (eBook, PDF)

Structuring, Trading and Risk Management

• Format: PDF

Produktbeschreibung

Handbook of Multi-Commodity Markets and Products Over recent decades, the marketplace has seen an increasing integration, not only among different types of commodity markets such as energy, agricultural, and metals, but also with financial markets. This trend raises important questions about how to identify and analyse opportunities in and manage risks of commodity products. The Handbook of Multi-Commodity Markets and Products offers traders, commodity brokers, and other professionals a practical and comprehensive manual that covers market structure and functioning, as well as the practice of trading across a wide range of commodity markets and products. Written in non-technical language, this important resource includes the information needed to begin to master the complexities of and to operate successfully in today's challenging and fluctuating commodity marketplace. Designed as a practical practitioner-orientated resource, the book includes a detailed overview of key markets - oil, coal, electricity, emissions, weather, industrial metals, freight, agricultural and foreign exchange - and contains a set of tools for analysing, pricing and managing risk for the individual markets. Market features and the main functioning rules of the markets in question are presented, along with the structure of basic financial products and standardised deals. A range of vital topics such as stochastic and econometric modelling, market structure analysis, contract engineering, as well as risk assessment and management are presented and discussed in detail with illustrative examples to commodity markets. The authors showcase how to structure and manage both simple and more complex multi-commodity deals. Addressing the issues of profit-making and risk management, the book reveals how to exploit pay-off profiles and trading strategies on a diversified set of commodity prices. In addition, the book explores how to price energy products and other commodities belonging to markets segmented across specific structural features. The Handbook of Multi-Commodity Markets and Products includes a wealth of proven methods and useful models that can be selected and developed in order to make appropriate estimations of the future evolution of prices and appropriate valuations of products. The authors additionally explore market risk issues and what measures of risk should be adopted for the purpose of accurately assessing exposure from multi-commodity portfolios. This vital resource offers the models, tools, strategies and general information commodity brokers and other professionals need to succeed in today's highly competitive marketplace.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 1064
• Erscheinungstermin: 17. Februar 2015
• Englisch
• ISBN-13: 9780470662502
• Artikelnr.: 42319905

Autorenporträt

ANDREA RONCORONI is Professor of Finance at ESSEC Business School (Paris-Singapore), regular Visiting Professor at Bocconi University (Milan), and Director of the ESSEC Energy and Commodity Finance research center. He holds PhD's in Applied Mathematics and in Finance. His research interests primarily cover energy and commodity markets, corporate financial risk analysis and management, quantitative modelling, derivative design and valuation. Andrea put forward the Threshold Model for price simulation in spiky electricity markets, and devised FloRisk Metrics, an effective analytics to monitor and manage corporate financial exposure. He publishes in academic journals, professional reviews, financial book series, and acts as Associate Editor for the Journal of Energy Markets and Co-Editor for Argo Review. Andrea has co-authored the reference volume Implementing Models in Quantitative Finance. As a professional advisor, he consulted for private companies and public institutions, including Dong Energy, Edison, Enel, GDF, Natixis, and Trafigura Electricity Italia (TEI Energy). He is founder and CEO of Energisk, a start-up company developing cutting-edge risk analytics for corporate clients. GIANLUCA FUSAI is Full Professor in Financial Mathematics at the University of Eastern Piedmont, Italy, and a PT Reader in Mathematical Finance at Cass Business School, City University of London, UK. He holds a PhD in Finance from Warwick Business School, an MSc in Statistics and Operational Research from the University of Essex and a BSc in Economics from Bocconi University. His research interests focus on Energy Markets, Financial Engineering, Numerical Methods for Finance, Quantitative Risk Management. He has published extensively on these topics in top-tier international reviews. Gianluca has also co-authored the best-selling textbook Implementing Models in Quantitative Finance. Gianluca has cooperated to several projects in energy markets including a multi-energy risk assessment tool developed in conjunction with a pool of energy and industrial companies and a forward curve builder for the power and gas markets nowadays used for trading and marking to market. He has also been a consultant for private and public sector on building pricing tools of derivative products. Gianluca has been an expert witness in several derivative disputes. MARK CUMMINS is Senior Lecturer in Finance at the Dublin City University Business School and holds a PhD in Quantitative Finance. Mark's research interests include a broad range of energy and commodity modelling, derivatives, risk management and trading topics. Mark has published in international journals such as Energy Economics, Applied Energy and the Journal of Energy Markets, as well as mainstream finance journals such as the Journal of Financial Markets, International Review of Financial Analysis and Quantitative Finance. Mark has previous industry experience working as a Quantitative Analyst within the Global Risk function for BP Oil International Ltd. As part of the Risk Quantitative Analysis team, primary responsibilities included derivatives and price curve model validation and development, with a global remit across BP's energy and commodity activities. Mark is engaged in ongoing industry training and consultancy activities, focused on the energy sector primarily.

Inhaltsangabe

Preface xix Acknowledgements xxiii About the Editors xxv List of Contributors xxvii Part One Commodity Markets and Products Chapter 1 Oil 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 Strategies 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 Chapter 2 Coal Markets and Products 67 Lars Schernikau 2.1 Introduction 67 2.2 Source of Coal - Synopsis of the Resource Coal 72 2.2.1 The Fundamentals of Energy Sources and Fossil Fuels 72 2.2.2 Process of Coal Formation 74 2.2.3 Coal Classification 74 2.2.4 Reserves and Resources 79 2.2.5 Coal Mining and Production 83 2.3 Use of Coal - Power Generation and More 90 2.3.1 Steam Coal and its Role in Power Generation 91 2.3.2 Coal-Fired Power Plant Technologies 93 2.3.3 Cement and Other Industry 95 2.3.4 Alternatives to Coal: Shale Gas and Other 95 2.3.5 Future Trend: CtL and Coal Bed Methane 101 2.4 Overview of Worldwide Steam Coal Supply and Demand 102 2.4.1 Atlantic Demand Market: Europe at its Core 102 2.4.2 Pacific Demand Market: China, India, Japan, Taiwan, Korea and SEA 104 2.4.3 Steam Coal Supply Regions: ID, AU, USA, SA, RU, CO and Others 107 2.4.4 Seaborne Freight 116 2.4.5 Geopolitical and Policy Environment 118 2.5 The Global Steam Coal Trade Market and its Future 121 2.5.1 Current and Future Market Dynamics of the Coal Trade 121 2.5.2 Future Steam Coal Price Trends 125 2.5.3 Future Source of Energy: What Role Will Coal Play? 127 2.6 Concluding Words 129 Abbreviations and Definitions 130 Acknowledgements 132 References 132 Chapter 3 Natural Gas Markets and Products 135 Mark Cummins and Bernard Murphy 3.1 Physical Natural Gas Markets 135 3.1.1 Physical Structure 141 3.1.2 Natural Gas Market Hubs and Main Participants 146 3.1.3 Liquefied Natural Gas 147 3.1.4 Shale Gas 149 3.2 Natural Gas Contracting and Pricing 154 3.2.1 Natural Gas Price Formation 155 3.3 Financial Natural Gas Markets 158 3.3.1 Exchange-Based Markets 158 3.3.2 Natural Gas Futures 159 3.3.3 Natural Gas Options 172 3.3.4 OTC Markets and Products 179 References 180 Chapter 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-Based 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: CO2 Emission Permits 241 5.6.1 Macroeconomic Models 242 5.6.2 Econometric Investigation of CO2 Permit Price Time-Series 243 5.6.3 Stochastic Equilibrium Models 251 Abbreviations 252 References 252 Chapter 6 Weather Risk and Weather Derivatives 255 Alessandro Mauro 6.1 Introduction 255 6.2 Identification of Volumetric 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 Volumetric 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 Chapter 7 Industrial 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 313 7.2.3 LME, COMEX and SHFE Copper Curve and Arbitrage 314 7.2.4 Contango Limit... 318 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 338 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 348 Acknowledgements 352 References 353 Further Reading 353 Chapter 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 Chapter 9 Agricultural and Soft Markets 399 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 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 496 Glossary, Quotations and Policy on Websites 497 Chapter 10 Foreign Exchange Markets and Products 499 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 10.2 Pricing Models for FX Options 509 10.2.1 The Black-Scholes Model 510 10.3 The Volatility Surface 511 10.4 Barrier Options 512 10.4.1 A Taxonomy of Barrier Options 512 10.5 Sources of FX Risk Exposure 513 10.6 Hedging FX Exposures Embedded in Energy and Commodity Contracts 517 10.6.1 FX Forward Exposures and Conversions 518 10.6.2 FX-Linked Energy Contracts 522 10.7 Typical Hedging Structures for FX Risk Exposure 533 10.7.1 Collar Plain Vanilla 533 10.7.2 Leveraged Forward 536 10.7.3 Participating Forward 538 10.7.4 Knock-Out Forward 540 10.7.5 Knock-In Forward 543 10.7.6 Knock-In Knock-out Forward 545 10.7.7 Resettable Forward 548 10.7.8 Range Resettable Forward 550 References 553 Part Two Quantitative Topics Chapter 11 An Introduction to Stochastic Calculus with Matlab® Examples 557 Laura Ballotta and Gianluca Fusai 11.1 Brownian Motion 558 11.1.1 Defining Brownian Motion 558 11.2 The Stochastic Integral and Stochastic Differential Equations 566 11.2.1 Introduction 566 11.2.2 Defining the Stochastic Integral 567 11.2.3 The It Stochastic Integral as a Mean Square Limit of Suitable Riemann-Stieltjes Sums 567 11.2.4 A Motivating Example: Computing
0tW(s)dW(s) 568 11.2.5 Properties of the Stochastic Integral 569 11.2.6 It
o Process and Stochastic Differential Equations 571 11.2.7 Solving Stochastic Integrals and/or Stochastic Differential Equations 573 11.3 Introducing It
's Formula 575 11.3.1 A Fact from Ordinary Calculus 576 11.3.2 It
o's Formula when Y = g(x), g(x)
C2 576 11.3.3 Guiding Principle 577 11.3.4 It
o's Formula when Y(t) = g(t, X), g(t, X)
C1,2 577 11.3.5 The Multivariate It
o's Lemma when Z = g(t, X, Y) 578 11.4 Important SDEs 581 11.4.1 The Geometric Brownian Motion GBM(
,
) 581 11.4.2 The Vasicek Mean-Reverting Process 588 11.4.3 The Cox-Ingersoll-Ross (CIR) Model 595 11.4.4 The Constant Elasticity of Variance (CEV) Model 604 11.4.5 The Brownian Bridge 607 11.4.6 The Stochastic Volatility Heston Model (1987) 611 11.5 Stochastic Processes with Jumps 618 11.5.1 Preliminaries 618 11.5.2 Jump Diffusion Processes 623 11.5.3 Time-Changed Brownian Motion 628 11.5.4 Final Remark: Lévy Processes 632 References 633 Further Reading 633 Chapter 12 Estimating Commodity Term Structure Volatilities 635 Andrea Roncoroni, Rachid Id Brik and Mark Cummins 12.1 Introduction 635 12.2 Model Estimation Using the Kalman Filter 635 12.2.1 Description of the Methodology 636 12.2.2 Case Study: Estimating Parameters on Crude Oil 642 12.3 Principal Components Analysis 646 12.3.1 PCA: Technical Presentation 647 12.3.2 Case Study: Risk Analysis on Energy Markets 651 12.4 Conclusion 655 Appendix 655 References 657 Chapter 13 Nonparametric Estimation of Energy and Commodity Price Processes 659 Gianna Fig`a-Talamanca and Andrea Roncoroni 13.1 Introduction 659 13.2 Estimation Method 660 13.3 Empirical Results 663 References 672 Chapter 14 How to Build Electricity Forward Curves 673 Ruggero Caldana, Gianluca Fusai and Andrea Roncoroni 14.1 Introduction 673 14.2 Review of the Literature 674 14.3 Electricity Forward Contracts 675 14.4 Smoothing Forward Price Curves 677 14.5 An Illustrative Example: Daily Forward Curve 679 14.6 Conclusion 684 References 684 Chapter 15 GARCH Models for Commodity Markets 687 Eduardo Rossi and Filippo Spazzini 15.1 Introduction 687 15.2 The GARCH Model: General Definition 690 15.2.1 The ARCH(q) Model 692 15.2.2 The GARCH(p,q) Model 693 15.2.3 The Yule-Walker Equations for the Squared Process 695 15.2.4 Stationarity of the GARCH(p,q) 696 15.2.5 Forecasting Volatility with GARCH 698 15.3 The IGARCH(p,q) Model 699 15.4 A Permanent and Transitory Component Model of Volatility 700 15.5 Asymmetric Models 702 15.5.1 The EGARCH(p,q) Model 702 15.5.2 Other Asymmetric 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 Asymmetric 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 Chapter 16 Pricing Commodity Swaps with Counterparty Credit Risk: The Case of Credit Value Adjustment 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 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 Chapter 17 Pricing 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 Zero 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 Chapter 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 Matching 835 18.2.2 Lower Price Bound 844 18.2.3 Monte carlo simulation 845 18.3 A Comparison 856 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 871 18.5 Conclusions 874 References 874 Chapter 19 Natural Gas Storage Modelling 877 A
lvaro Cartea, James Cheeseman and Sebastian Jaimungal 19.1 Introduction 877 19.2 A Simple Model of Storage, Futures Prices, Spot Prices And Convenience Yield 878 19.3 Valuation of Gas Storage 880 19.3.1 Least-Squares Monte Carlo 881 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 Chapter 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 Chapter 21 Econometric Analysis of Energy and Commodity Markets: Multiple Hypothesis Testing Techniques 939 Mark Cummins 21.1 Introduction 939 21.2 Multiple Hypothesis Testing 940 21.2.1 Generalized Familywise Error Rate 941 21.2.2 Per-Familywise Error Rate 942 21.2.3 False Discovery Proportion 942 21.2.4 False Discovery Rate 943 21.2.5 Single-Step and Stepwise Procedures 943 21.3 Energy-Emissions Market Interactions 943 21.3.1 Literature Review 943 21.3.2 Data Description 944 21.3.3 Testing Framework 945 21.3.4 Empirical Results 950 21.4 Emissions Market Interactions 953 21.4.1 Testing Framework and Data 953 21.4.2 Empirical Results 955 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