Handbook of Multi-Commodity Markets and Products
Structuring, Trading and Risk Management
The Wiley Finance Series

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.

Preface xix

Acknowledgements xxiii

About the Editors xxv

List of Contributors xxvii

Part One Commodity Markets and Products

Chapter 1 Oil Markets and Products3
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 Products67
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 Products135
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 Products181
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 Products223
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 Products279
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 Products355
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 Markets399
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 Products499
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 ∫₀^tW(s)dW(s) 568

11.2.5 Properties of the Stochastic Integral 569

11.2.6 Itˆo Process and Stochastic Differential Equations 571

11.2.7 Solving Stochastic Integrals and/or Stochastic Differential Equations 573

11.3 Introducing Itȏ’s Formula 575

11.3.1 A Fact from Ordinary Calculus 576

11.3.2 Itˆo’s Formula when Y = g(x), g(x) ∈ C² 576

11.3.3 Guiding Principle 577

11.3.4 Itˆo’s Formula when Y(t) = g(t, X), g(t, X) ∈ C^1,2 577

11.3.5 The Multivariate Itˆo’s Lemma when Z = g(t, X, Y) 578

11.4 Important SDEs 581

11.4.1 The Geometric Brownian Motion GBM(𝜇, 𝜎) 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

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 energ