Handbook of Multi-Commodity Markets and Products

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.

• Preface xixAcknowledgements xxiiiAbout the Editors xxvList of Contributors xxviiPart One Commodity Markets and ProductsChapter 1 Oil Markets and Products 3Cristiano Campi and Francesco Galdenzi1.1 Introduction 31.2 Risk Management for Corporations: Hedging Using Derivative Instruments 41.2.1 Crude Oil and Oil Products Risk Management for Corporations 41.2.2 Aviation: Risk Profile and Hedging Strategies 111.2.3 Shipping: Risk Profile and Hedging Strategies 201.2.4 Land Transportation: Risk Profile and Hedging Strategies 271.2.5 Utilities: Risk Profile and Hedging Strategies 321.2.6 Refineries: Risk Profile and Hedging Strategies 351.2.7 Industrial Consumers: Risk Profile and Hedging Strategies 401.3 Oil Physical Market Hedging and Trading 411.3.1 The Actors, Futures and OTC Prices 411.3.2 The Most Commonly Used Financial Instruments 451.3.3 How to Monitor and Manage Risk 491.3.4 How to Create a Market View 521.3.5 Trading Strategies to Maximize a Market View 54Further Reading 66Chapter 2 Coal Markets and Products 67Lars Schernikau2.1 Introduction 672.2 Source of Coal – Synopsis of the Resource Coal 722.2.1 The Fundamentals of Energy Sources and Fossil Fuels 722.2.2 Process of Coal Formation 742.2.3 Coal Classification 742.2.4 Reserves and Resources 792.2.5 Coal Mining and Production 832.3 Use of Coal – Power Generation and More 902.3.1 Steam Coal and its Role in Power Generation 912.3.2 Coal-Fired Power Plant Technologies 932.3.3 Cement and Other Industry 952.3.4 Alternatives to Coal: Shale Gas and Other 952.3.5 Future Trend: CtL and Coal Bed Methane 1012.4 Overview of Worldwide Steam Coal Supply and Demand 1022.4.1 Atlantic Demand Market: Europe at its Core 1022.4.2 Pacific Demand Market: China, India, Japan, Taiwan, Korea and SEA 1042.4.3 Steam Coal Supply Regions: ID, AU, USA, SA, RU, CO and Others 1072.4.4 Seaborne Freight 1162.4.5 Geopolitical and Policy Environment 1182.5 The Global Steam Coal Trade Market and its Future 1212.5.1 Current and Future Market Dynamics of the Coal Trade 1212.5.2 Future Steam Coal Price Trends 1252.5.3 Future Source of Energy: What Role Will Coal Play? 1272.6 Concluding Words 129Abbreviations and Definitions 130Acknowledgements 132References 132Chapter 3 Natural Gas Markets and Products 135Mark Cummins and Bernard Murphy3.1 Physical Natural Gas Markets 1353.1.1 Physical Structure 1413.1.2 Natural Gas Market Hubs and Main Participants 1463.1.3 Liquefied Natural Gas 1473.1.4 Shale Gas 1493.2 Natural Gas Contracting and Pricing 1543.2.1 Natural Gas Price Formation 1553.3 Financial Natural Gas Markets 1583.3.1 Exchange-Based Markets 1583.3.2 Natural Gas Futures 1593.3.3 Natural Gas Options 1723.3.4 OTC Markets and Products 179References 180Chapter 4 Electricity Markets and Products 181Stefano Fiorenzani, Bernard Murphy and Mark Cummins4.1 Market Structure and Price Components 1814.1.1 Spot and Forward Markets 1814.1.2 Supply and Demand Interaction 1834.1.3 Electricity Derivatives 1864.1.4 Power Price Models 1894.1.5 Spot Price Analysis (IPEX Case) 1964.1.6 Forward Price Analysis (EEX Case) 2004.2 Renewables, Intra-Day Trading and Capacity Markets 2054.2.1 Renewables Expansion Targets 2054.2.2 Growth in Intra-Day Trading 2064.2.3 Implications for Future Price Volatility and Price Profiles 2074.2.4 Reforms and Innovations in Capacity Markets 2094.2.5 Provision and Remuneration of Flexibility – Storage Assets 2124.3 Risk Measures for Power Portfolios 2164.3.1 Value-Based Risk Measures 2164.3.2 Flow-Based Risk Measures 2184.3.3 Credit Risk for Power Portfolios 220References 221Further Reading 221Chapter 5 Emissions Markets and Products 223Marc Chesney, Luca Taschini and Jonathan Gheyssens5.1 Introduction 2235.2 Climate Change and the Economics of Externalities 2245.2.1 The Climate Change Issue 2245.2.2 The Economics of Externality and GHG Pollution 2265.3 The Kyoto Protocol 2275.3.1 The United Nations Framework Convention on Climate Change 2275.3.2 The Conference of Parties and the Subsidiary Bodies 2295.3.3 The Kyoto Protocol 2295.3.4 The Road to Paris 2315.4 The EU ETS 2325.4.1 Institutional Features 2325.4.2 Allocation Criteria 2345.4.3 Market Players and the Permit Markets 2365.4.4 The Future of the EU ETS 2385.5 Regional Markets: A Fragmented Landscape 2395.5.1 Regional Markets 2395.5.2 Voluntary Markets 2405.6 A New Asset Class: CO2 Emission Permits 2415.6.1 Macroeconomic Models 2425.6.2 Econometric Investigation of CO2 Permit Price Time-Series 2435.6.3 Stochastic Equilibrium Models 251Abbreviations 252References 252Chapter 6 Weather Risk and Weather Derivatives 255Alessandro Mauro6.1 Introduction 2556.2 Identification of Volumetric Risk 2576.2.1 Weather Events on the Demand Curve 2586.2.2 Weather Events on the Supply Curve 2606.2.3 Risk Measurement and Weather-at-Risk 2626.3 Atmospheric Temperature and Natural Gas Market 2646.3.1 Characterization of the Air Temperature Meteorological Variable 2646.3.2 Degree Days 2676.3.3 Volumetric Risk in the Natural Gas Market 2706.4 Modification of Weather Risk Exposure with Weather Derivatives 2726.4.1 Weather Derivatives for Temperature-Related Risk 2736.5 Conclusions 276Nomenclature 277References 277Chapter 7 Industrial Metals Markets and Products 279Alessandro Porru7.1 General Overview 2797.1.1 Brief History of the LME 2807.1.2 Non-ferrous Metals 2827.1.3 Other Metals 2917.1.4 LME Instruments 2927.1.5 OTC Instruments 2987.1.6 A New Player: The Investor 3017.2 Forward Curves 3057.2.1 Building LME’s Curves in Practice 3087.2.2 Interpolation 3137.2.3 LME, COMEX and SHFE Copper Curve and Arbitrage 3147.2.4 Contango Limit… 3187.2.5 …and No-Limit Backwardation 3247.2.6 Hedging the Curve in Practice 3287.3 Volatility 3377.3.1 A European Disguised as an American 3387.3.2 LME’s Closing Volatilities 3397.3.3 Sticky Strike, Sticky Delta and Skew 3427.3.4 Building the Surface in Practice 3457.3.5 Considerations on Vega Hedging 348Acknowledgements 352References 353Further Reading 353Chapter 8 Freight Markets and Products 355Manolis G. Kavussanos, Ilias D. Visvikis and Dimitris N. Dimitrakopoulos8.1 Introduction 3558.2 Business Risks in Shipping 3568.2.1 The Sources of Risk in the Shipping Industry 3568.2.2 Market Segmentation in the Shipping Industry 3588.2.3 Empirical Regularities in Freight Rate Markets 3598.2.4 Traditional Risk Management Strategies 3658.3 Freight Rate Derivatives 3668.3.1 Risk Management in Shipping 3668.3.2 The Underlying Indices of Freight Rate Derivatives 3668.3.3 The Freight Derivatives Market 3728.3.4 Examples of Freight Derivatives Trading 3808.4 Pricing, Hedging and Freight Rate Risk Measurement 3828.4.1 Pricing and Hedging Effectiveness of Freight Derivatives 3828.4.2 Value-at-Risk (VaR) in Freight Markets 3848.4.3 Expected Shortfall (ES) in Freight Markets 3898.4.4 Empirical Evidence on Freight Derivatives 3908.5 Other Derivatives for the Shipping Industry 3938.5.1 Bunker Fuel Derivatives 3938.5.2 Vessel Value Derivatives 3958.5.3 Foreign Exchange Rate Derivatives Contracts 3958.5.4 Interest Rate Derivatives Contracts 3968.6 Conclusion 396Acknowledgements 396References 397Chapter 9 Agricultural and Soft Markets 399Francis Declerk9.1 Introduction: Stakes and Objectives 3999.1.1 Stakes 3999.1.2 Objectives 3999.2 Agricultural Commodity Specificity and Futures Markets 4009.2.1 Agricultural Commodity Specificity 4009.2.2 Volatility of Agricultural Markets 4029.2.3 Forward Contract and Futures Contract 4029.2.4 Major Agricultural Futures Markets and Contracts 4049.2.5 Roles of Futures Markets 4059.2.6 Institutions Related to Futures Markets 4069.2.7 Commodity Futures Contracts 4069.2.8 The Operators 4089.2.9 Monitoring Hedging: Settlement 4099.2.10 Accounting and Tax Rules 4099.3 Demand and Supply, Price Determinants and Dynamics 4099.3.1 Supply and Demand for Agricultural Commodities: The Determinants 4099.3.2 Agricultural Market Prices, Failures and Policies 4139.3.3 The Price Dynamics of Seasonal and Storable Agricultural Commodities 4169.3.4 The Features of Major Agricultural and Soft Markets 4179.4 Hedging and Basis Management 4669.4.1 Short Hedging for Producers 4669.4.2 Long Hedging for Processors 4699.4.3 Management of Basis Risk 4719.5 The Financialization of Agricultural Markets and Hunger: Speculation and Regulation 4809.5.1 Factors Affecting the Volatility of Agricultural Commodity Prices 4809.5.2 Financialization: Impact of Non-commercial Traders on Market Price 4839.5.3 The Financialization of Grain Markets and Speculation 4849.5.4 Bubble or Not, Agricultural Commodities have Become an Asset Class 4899.5.5 Price Volatility and Regulation 4909.5.6 Ongoing Research about Speculation and Regulation 4939.6 Conclusion about Hedging and Futures Contracts 4939.6.1 Hedging Process 4939.6.2 Key Success Factors for Agricultural Commodity Futures Contracts 4949.6.3 Conclusion and Prospects 495References 495Further Reading 496Glossary, Quotations and Policy on Websites 497Chapter 10 Foreign Exchange Markets and Products 499Antonio Castagna10.1 The FX Market 49910.1.1 FX Rates and Spot Contracts 49910.1.2 Outright and FX Swap Contracts 50010.1.3 FX Option Contracts 50410.1.4 Main Traded FX Options Structures 50710.2 Pricing Models for FX Options 50910.2.1 The Black–Scholes Model 51010.3 The Volatility Surface 51110.4 Barrier Options 51210.4.1 A Taxonomy of Barrier Options 51210.5 Sources of FX Risk Exposure 51310.6 Hedging FX Exposures Embedded in Energy and Commodity Contracts 51710.6.1 FX Forward Exposures and Conversions 51810.6.2 FX-Linked Energy Contracts 52210.7 Typical Hedging Structures for FX Risk Exposure 53310.7.1 Collar Plain Vanilla 53310.7.2 Leveraged Forward 53610.7.3 Participating Forward 53810.7.4 Knock-Out Forward 54010.7.5 Knock-In Forward 54310.7.6 Knock-In Knock-out Forward 54510.7.7 Resettable Forward 54810.7.8 Range Resettable Forward 550References 553Part Two Quantitative TopicsChapter 11 An Introduction to Stochastic Calculus with Matlab® Examples 557Laura Ballotta and Gianluca Fusai11.1 Brownian Motion 55811.1.1 Defining Brownian Motion 55811.2 The Stochastic Integral and Stochastic Differential Equations 56611.2.1 Introduction 56611.2.2 Defining the Stochastic Integral 56711.2.3 The It Stochastic Integral as a Mean Square Limit of Suitable Riemann–Stieltjes Sums 56711.2.4 A Motivating Example: Computing ∫0tW(s)dW(s) 56811.2.5 Properties of the Stochastic Integral 56911.2.6 Itˆo Process and Stochastic Differential Equations 57111.2.7 Solving Stochastic Integrals and/or Stochastic Differential Equations 57311.3 Introducing Itȏ’s Formula 57511.3.1 A Fact from Ordinary Calculus 57611.3.2 Itˆo’s Formula when Y = g(x), g(x) ∈ C2 57611.3.3 Guiding Principle 57711.3.4 Itˆo’s Formula when Y(t) = g(t, X), g(t, X) ∈ C1,2 57711.3.5 The Multivariate Itˆo’s Lemma when Z = g(t, X, Y) 57811.4 Important SDEs 58111.4.1 The Geometric Brownian Motion GBM(𝜇, 𝜎) 58111.4.2 The Vasicek Mean-Reverting Process 58811.4.3 The Cox–Ingersoll–Ross (CIR) Model 59511.4.4 The Constant Elasticity of Variance (CEV) Model 60411.4.5 The Brownian Bridge 60711.4.6 The Stochastic Volatility Heston Model (1987) 61111.5 Stochastic Processes with Jumps 61811.5.1 Preliminaries 61811.5.2 Jump Diffusion Processes 62311.5.3 Time-Changed Brownian Motion 62811.5.4 Final Remark: Lévy Processes 632References 633Further Reading 633Chapter 12 Estimating Commodity Term Structure Volatilities 635Andrea Roncoroni, Rachid Id Brik and Mark Cummins12.1 Introduction 63512.2 Model Estimation Using the Kalman Filter 63512.2.1 Description of the Methodology 63612.2.2 Case Study: Estimating Parameters on Crude Oil 64212.3 Principal Components Analysis 64612.3.1 PCA: Technical Presentation 64712.3.2 Case Study: Risk Analysis on Energy Markets 65112.4 Conclusion 655Appendix 655References 657Chapter 13 Nonparametric Estimation of Energy and Commodity Price Processes 659Gianna Fig`a-Talamanca and Andrea Roncoroni13.1 Introduction 65913.2 Estimation Method 66013.3 Empirical Results 663References 672Chapter 14 How to Build Electricity Forward Curves 673Ruggero Caldana, Gianluca Fusai and Andrea Roncoroni14.1 Introduction 67314.2 Review of the Literature 67414.3 Electricity Forward Contracts 67514.4 Smoothing Forward Price Curves 67714.5 An Illustrative Example: Daily Forward Curve 67914.6 Conclusion 684References 684Chapter 15 GARCH Models for Commodity Markets 687Eduardo Rossi and Filippo Spazzini15.1 Introduction 68715.2 The GARCH Model: General Definition 69015.2.1 The ARCH(q) Model 69215.2.2 The GARCH(p,q) Model 69315.2.3 The Yule–Walker Equations for the Squared Process 69515.2.4 Stationarity of the GARCH(p,q) 69615.2.5 Forecasting Volatility with GARCH 69815.3 The IGARCH(p,q) Model 69915.4 A Permanent and Transitory Component Model of Volatility 70015.5 Asymmetric Models 70215.5.1 The EGARCH(p,q) Model 70215.5.2 Other Asymmetric Models 70415.5.3 The News Impact Curve 70615.6 Periodic GARCH 70715.6.1 Periodic EGARCH 70815.7 Nesting Models 70815.8 Long-Memory GARCH Models 71315.8.1 The FIGARCH Model 71615.8.2 The FIEGARCH Model 71915.9 Estimation 72015.9.1 Likelihood Computation 72015.10 Inference 72215.10.1 Testing for ARCH Effects 72215.10.2 Test for Asymmetric Effects 72315.11 Multivariate GARCH 72515.11.1 BEKK Parameterization of MGARCH 72615.11.2 The Dynamic Conditional Correlation Model 72615.12 Empirical Applications 72715.12.1 Univariate Volatility Modelling 72715.12.2 A Simple Risk Measurement Application: A Bivariate Example with Copulas 73315.13 Software 740References 748Chapter 16 Pricing Commodity Swaps with Counterparty Credit Risk: The Case of Credit Value Adjustment 755Marina Marena, Gianluca Fusai and Chiara Quaglini16.1 Introduction 75516.1.1 Energy Company Strategies in Derivative Instruments 75516.2 Company Energy Policy 75616.2.1 Commodity Risk 75616.2.2 Credit Risk 75716.3 A Focus on Commodity Swap Contracts 75816.3.1 Definition and Main Features of a Commodity Swap 75816.4 Modelling the Dynamics of Oil Spot Prices and the Forward Curve 76016.4.1 The Schwartz and Smith Pricing Model 76016.5 An Empirical Application 76416.5.1 The Commodity Swap Features 76416.5.2 Calibration of the Theoretical Schwartz and Smith Forward Curve 76516.5.3 The Monte Carlo Simulation of Oil Spot Prices 77216.5.4 The Computation of Brent Forward Curves at Any Given Valuation Date 77316.6 Measuring Counterparty Risk 77716.6.1 CVA Calculation 77916.6.2 Swap Fixed Price Adjustment for Counterparty Risk 78216.6.3 Right- and Wrong-Way Risk 78416.7 Sensitivity Analysis 78816.8 Accounting for Derivatives and Credit Value Adjustments 78816.8.1 Example of Hedge Effectiveness 79116.8.2 Accounting for CVA 79616.9 Conclusions 797References 798Further Reading 798Chapter 17 Pricing Energy Spread Options 801Fred Espen Benth and Hanna Zdanowicz17.1 Spread Options in Energy Markets 80117.2 Pricing of Spread Options with Zero Strike 80517.3 Issues of hedging 81317.4 Pricing of Spread Options with Nonzero Strike 81517.4.1 Kirk’s Approximation Formula 81717.4.2 Approximation by Margrabe Based on Taylor Expansion 82017.4.3 Other Pricing Methods 823Acknowledgement 824References 825Chapter 18 Asian Options: Payoffs and Pricing Models 827Gianluca Fusai, Marina Marena and Giovanni Longo18.1 Payoff Structures 83218.2 Pricing Asian Options in the Lognormal Setting 83318.2.1 Moment Matching 83518.2.2 Lower Price Bound 84418.2.3 Monte carlo simulation 84518.3 A Comparison 85618.4 The Flexible Square-Root Model 85818.4.1 General Setup 86118.4.2 Numerical Results 87018.4.3 A Case Study 87118.5 Conclusions 874References 874Chapter 19 Natural Gas Storage Modelling 877A´lvaro Cartea, James Cheeseman and Sebastian Jaimungal19.1 Introduction 87719.2 A Simple Model of Storage, Futures Prices, Spot Prices And Convenience Yield 87819.3 Valuation of Gas Storage 88019.3.1 Least-Squares Monte Carlo 88119.3.2 LSMC Greeks 88319.3.3 Extending the LSMC to Price Gas Storage 88319.3.4 Toy Storage Model 88419.3.5 Storage LSMC 88819.3.6 Swing Options 89019.3.7 Closed-Form Storage Solution 89119.3.8 Monte Carlo Convergence 89219.3.9 Simulated Storage Operations 89419.3.10 Storage Value 897References 899Chapter 20 Commodity-Linked Arbitrage Strategies and Portfolio Management 901Viviana Fanelli20.1 Commodity-Linked Arbitrage Strategies 90220.1.1 The Efficient Market Hypothesis 90220.1.2 Risk Arbitrage Opportunities in Commodity Markets 90320.1.3 Basic Quantitative Trading Strategies 90620.1.4 A General Statistical Arbitrage Trading Methodology 91420.2 Portfolio Optimization with Commodities 92120.2.1 Commodities as an Asset Class 92120.2.2 Commodity Futures Return Characteristics 92320.2.3 Risk Premiums in Commodity Markets 92520.2.4 Commodities as a Portfolio Diversifier 92820.2.5 Risk–Return Optimization in Commodity Portfolios 929 Symbols 936References 936Chapter 21 Econometric Analysis of Energy and Commodity Markets: Multiple Hypothesis Testing Techniques 939Mark Cummins21.1 Introduction 93921.2 Multiple Hypothesis Testing 94021.2.1 Generalized Familywise Error Rate 94121.2.2 Per-Familywise Error Rate 94221.2.3 False Discovery Proportion 94221.2.4 False Discovery Rate 94321.2.5 Single-Step and Stepwise Procedures 94321.3 Energy–Emissions Market Interactions 94321.3.1 Literature Review 94321.3.2 Data Description 94421.3.3 Testing Framework 94521.3.4 Empirical Results 95021.4 Emissions Market Interactions 95321.4.1 Testing Framework and Data 95321.4.2 Empirical Results 95521.5 Quantitative Spread Trading in Oil Markets 95621.5.1 Testing Framework and Data 95621.5.2 Optimal Statistical Arbitrage Model 95721.5.3 Resampling-Based MHT Procedures 95921.5.4 Empirical Results 964References 964Appendix A Quick Review of Distributions Relevant in Finance with Matlab® Examples 967Laura Ballotta and Gianluca FusaiIndex 1005