Muutke küpsiste eelistusi

E-raamat: Handbook of Multi-Commodity Markets and Products - Structuring, Trading and Risk Management: Structuring, Trading and Risk Management [Wiley Online]

(University of Paris and ESSEC, France), (Universit¿ degli Studi del Piemonte, Italy),
  • Formaat: 1072 pages
  • Sari: The Wiley Finance Series
  • Ilmumisaeg: 10-Mar-2015
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119011590
  • ISBN-13: 9781119011590
Teised raamatud teemal:
  • Wiley Online
  • Hind: 169,17 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
Handbook of Multi-Commodity Markets and Products - Structuring, Trading and Risk Management: Structuring, Trading and Risk ManagementSuurem pilt
  • Formaat: 1072 pages
  • Sari: The Wiley Finance Series
  • Ilmumisaeg: 10-Mar-2015
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119011590
  • ISBN-13: 9781119011590
Teised raamatud teemal:
Wiley Online e-raamatudRohkem infot Wiley Online kohta
Raamatu kodulehekülg: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119011590
Handbook of Multi-Commodity Markets and ProductsOver 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 todays 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 todays 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 Products
3(64)
Cristiano Campi
Francesco Galdenzi
1.1 Introduction
3(1)
1.2 Risk Management for Corporations: Hedging Using Derivative Instruments
4(37)
1.2.1 Crude Oil and Oil Products Risk Management for Corporations
4(7)
1.2.2 Aviation: Risk Profile and Hedging Strategies
11(9)
1.2.3 Shipping: Risk Profile and Hedging Strategies
20(7)
1.2.4 Land Transportation: Risk Profile and Hedging Strategies
27(5)
1.2.5 Utilities: Risk Profile and Hedging Strategies
32(3)
1.2.6 Refineries: Risk Profile and Hedging Strategies
35(5)
1.2.7 Industrial Consumers: Risk Profile and Hedging Strategies
40(1)
1.3 Oil Physical Market Hedging and Trading
41(26)
1.3.1 The Actors, Futures and OTC Prices
41(4)
1.3.2 The Most Commonly Used Financial Instruments
45(4)
1.3.3 How to Monitor and Manage Risk
49(3)
1.3.4 How to Create a Market View
52(2)
1.3.5 Trading Strategies to Maximize a Market View
54(12)
Further Reading
66(1)
Chapter 2 Coal Markets and Products
67(68)
Lars Schernikau
2.1 Introduction
67(5)
2.2 Source of Coal - Synopsis of the Resource Coal
72(18)
2.2.1 The Fundamentals of Energy Sources and Fossil Fuels
72(2)
2.2.2 Process of Coal Formation
74(1)
2.2.3 Coal Classification
74(5)
2.2.4 Reserves and Resources
79(4)
2.2.5 Coal Mining and Production
83(7)
2.3 Use of Coal - Power Generation and More
90(12)
2.3.1 Steam Coal and its Role in Power Generation
91(2)
2.3.2 Coal-Fired Power Plant Technologies
93(2)
2.3.3 Cement and Other Industry
95(1)
2.3.4 Alternatives to Coal: Shale Gas and Other
95(6)
2.3.5 Future Trend: CtL and Coal Bed Methane
101(1)
2.4 Overview of Worldwide Steam Coal Supply and Demand
102(19)
2.4.1 Atlantic Demand Market: Europe at its Core
102(2)
2.4.2 Pacific Demand Market: China, India, Japan, Taiwan, Korea and SEA
104(3)
2.4.3 Steam Coal Supply Regions: ID, AU, USA, SA, RU, CO and Others
107(9)
2.4.4 Seaborne Freight
116(2)
2.4.5 Geopolitical and Policy Environment
118(3)
2.5 The Global Steam Coal Trade Market and its Future
121(8)
2.5.1 Current and Future Market Dynamics of the Coal Trade
121(4)
2.5.2 Future Steam Coal Price Trends
125(2)
2.5.3 Future Source of Energy: What Role Will Coal Play?
127(2)
2.6 Concluding Words
129(6)
Abbreviations and Definitions
130(2)
Acknowledgements
132(1)
References
132(3)
Chapter 3 Natural Gas Markets and Products
135(46)
Mark Cummins
Bernard Murphy
3.1 Physical Natural Gas Markets
135(19)
3.1.1 Physical Structure
141(5)
3.1.2 Natural Gas Market Hubs and Main Participants
146(1)
3.1.3 Liquefied Natural Gas
147(2)
3.1.4 Shale Gas
149(5)
3.2 Natural Gas Contracting and Pricing
154(4)
3.2.1 Natural Gas Price Formation
155(3)
3.3 Financial Natural Gas Markets
158(23)
3.3.1 Exchange-Based Markets
158(1)
3.3.2 Natural Gas Futures
159(13)
3.3.3 Natural Gas Options
172(7)
3.3.4 OTC Markets and Products
179(1)
References
180(1)
Chapter 4 Electricity Markets and Products
181(42)
Stefano Fiorenzani
Bernard Murphy
Mark Cummins
4.1 Market Structure and Price Components
181(24)
4.1.1 Spot and Forward Markets
181(2)
4.1.2 Supply and Demand Interaction
183(3)
4.1.3 Electricity Derivatives
186(3)
4.1.4 Power Price Models
189(7)
4.1.5 Spot Price Analysis (IPEX Case)
196(4)
4.1.6 Forward Price Analysis (EEX Case)
200(5)
4.2 Renewables, Intra-Day Trading and Capacity Markets
205(11)
4.2.1 Renewables Expansion Targets
205(1)
4.2.2 Growth in Intra-Day Trading
206(1)
4.2.3 Implications for Future Price Volatility and Price Profiles
207(2)
4.2.4 Reforms and Innovations in Capacity Markets
209(3)
4.2.5 Provision and Remuneration of Flexibility - Storage Assets
212(4)
4.3 Risk Measures for Power Portfolios
216(7)
4.3.1 Value-Based Risk Measures
216(2)
4.3.2 Flow-Based Risk Measures
218(2)
4.3.3 Credit Risk for Power Portfolios
220(1)
References
221(1)
Further Reading
221(2)
Chapter 5 Emissions Markets and Products
223(32)
Marc Chesney
Luca Taschini
Jonathan Gheyssens
5.1 Introduction
223(1)
5.2 Climate Change and the Economics of Externalities
224(3)
5.2.1 The Climate Change Issue
224(2)
5.2.2 The Economics of Externality and GHG Pollution
226(1)
5.3 The Kyoto Protocol
227(5)
5.3.1 The United Nations Framework Convention on Climate Change
227(2)
5.3.2 The Conference of Parties and the Subsidiary Bodies
229(1)
5.3.3 The Kyoto Protocol
229(2)
5.3.4 The Road to Paris
231(1)
5.4 The EU ETS
232(7)
5.4.1 Institutional Features
232(2)
5.4.2 Allocation Criteria
234(2)
5.4.3 Market Players and the Permit Markets
236(2)
5.4.4 The Future of the EU ETS
238(1)
5.5 Regional Markets: A Fragmented Landscape
239(2)
5.5.1 Regional Markets
239(1)
5.5.2 Voluntary Markets
240(1)
5.6 A New Asset Class: CO2 Emission Permits
241(14)
5.6.1 Macroeconomic Models
242(1)
5.6.2 Econometric Investigation of CO 2 Permit Price Time-Series
243(8)
5.6.3 Stochastic Equilibrium Models
251(1)
Abbreviations
252(1)
References
252(3)
Chapter 6 Weather Risk and Weather Derivatives
255(24)
Alessandro Mauro
6.1 Introduction
255(2)
6.2 Identification of Volumetric Risk
257(7)
6.2.1 Weather Events on the Demand Curve
258(2)
6.2.2 Weather Events on the Supply Curve
260(2)
6.2.3 Risk Measurement and Weather-at-Risk
262(2)
6.3 Atmospheric Temperature and Natural Gas Market
264(8)
6.3.1 Characterization of the Air Temperature Meteorological Variable
264(3)
6.3.2 Degree Days
267(3)
6.3.3 Volumetric Risk in the Natural Gas Market
270(2)
6.4 Modification of Weather Risk Exposure with Weather Derivatives
272(4)
6.4.1 Weather Derivatives for Temperature-Related Risk
273(3)
6.5 Conclusions
276(3)
Nomenclature
277(1)
References
277(2)
Chapter 7 Industrial Metals Markets and Products
279(76)
Alessandro Porru
7.1 General Overview
279(26)
7.1.1 Brief History of the LME
280(2)
7.1.2 Non-ferrous Metals
282(9)
7.1.3 Other Metals
291(1)
7.1.4 LME Instruments
292(6)
7.1.5 OTC Instruments
298(3)
7.1.6 A New Player: The Investor
301(4)
7.2 Forward Curves
305(32)
7.2.1 Building LME's Curves in Practice
308(5)
7.2.2 Interpolation
313(1)
7.2.3 LME, COMEX and SHFE Copper Curve and Arbitrage
314(4)
7.2.4 Contango Limit...
318(6)
7.2.5 ...and No-Limit Backwardation
324(4)
7.2.6 Hedging the Curve in Practice
328(9)
7.3 Volatility
337(18)
7.3.1 A European Disguised as an American
338(1)
7.3.2 LME's Closing Volatilities
339(3)
7.3.3 Sticky Strike, Sticky Delta and Skew
342(3)
7.3.4 Building the Surface in Practice
345(3)
7.3.5 Considerations on Vega Hedging
348(4)
Acknowledgements
352(1)
References
353(1)
Further Reading
353(2)
Chapter 8 Freight Markets and Products
355(44)
Manolis G. Kavussanos
Ilias D. Visvikis
Dimitris N. Dimitrakopoulos
8.1 Introduction
355(1)
8.2 Business Risks in Shipping
356(10)
8.2.1 The Sources of Risk in the Shipping Industry
356(2)
8.2.2 Market Segmentation in the Shipping Industry
358(1)
8.2.3 Empirical Regularities in Freight Rate Markets
359(6)
8.2.4 Traditional Risk Management Strategies
365(1)
8.3 Freight Rate Derivatives
366(16)
8.3.1 Risk Management in Shipping
366(1)
8.3.2 The Underlying Indices of Freight Rate Derivatives
366(6)
8.3.3 The Freight Derivatives Market
372(8)
8.3.4 Examples of Freight Derivatives Trading
380(2)
8.4 Pricing, Hedging and Freight Rate Risk Measurement
382(11)
8.4.1 Pricing and Hedging Effectiveness of Freight Derivatives
382(2)
8.4.2 Value-at-Risk (VaR) in Freight Markets
384(5)
8.4.3 Expected Shortfall (ES) in Freight Markets
389(1)
8.4.4 Empirical Evidence on Freight Derivatives
390(3)
8.5 Other Derivatives for the Shipping Industry
393(3)
8.5.1 Bunker Fuel Derivatives
393(2)
8.5.2 Vessel Value Derivatives
395(1)
8.5.3 Foreign Exchange Rate Derivatives Contracts
395(1)
8.5.4 Interest Rate Derivatives Contracts
396(1)
8.6 Conclusion
396(3)
Acknowledgements
396(1)
References
397(2)
Chapter 9 Agricultural and Soft Markets
399(100)
Francis Declerk
9.1 Introduction: Stakes and Objectives
399(1)
9.1.1 Stakes
399(1)
9.1.2 Objectives
399(1)
9.2 Agricultural Commodity Specificity and Futures Markets
400(9)
9.2.1 Agricultural Commodity Specificity
400(2)
9.2.2 Volatility of Agricultural Markets
402(1)
9.2.3 Forward Contract and Futures Contract
402(2)
9.2.4 Major Agricultural Futures Markets and Contracts
404(1)
9.2.5 Roles of Futures Markets
405(1)
9.2.6 Institutions Related to Futures Markets
406(1)
9.2.7 Commodity Futures Contracts
406(2)
9.2.8 The Operators
408(1)
9.2.9 Monitoring Hedging: Settlement
409(1)
9.2.10 Accounting and Tax Rules
409(1)
9.3 Demand and Supply, Price Determinants and Dynamics
409(57)
9.3.1 Supply and Demand for Agricultural Commodities: The Determinants
409(4)
9.3.2 Agricultural Market Prices, Failures and Policies
413(3)
9.3.3 The Price Dynamics of Seasonal and Storable Agricultural Commodities
416(1)
9.3.4 The Features of Major Agricultural and Soft Markets
417(49)
9.4 Hedging and Basis Management
466(14)
9.4.1 Short Hedging for Producers
466(3)
9.4.2 Long Hedging for Processors
469(2)
9.4.3 Management of Basis Risk
471(9)
9.5 The Financialization of Agricultural Markets and Hunger: Speculation and Regulation
480(13)
9.5.1 Factors Affecting the Volatility of Agricultural Commodity Prices
480(3)
9.5.2 Financialization: Impact of Non-commercial Traders on Market Price
483(1)
9.5.3 The Financialization of Grain Markets and Speculation
484(5)
9.5.4 Bubble or Not, Agricultural Commodities have Become an Asset Class
489(1)
9.5.5 Price Volatility and Regulation
490(3)
9.5.6 Ongoing Research about Speculation and Regulation
493(1)
9.6 Conclusion about Hedging and Futures Contracts
493(6)
9.6.1 Hedging Process
493(1)
9.6.2 Key Success Factors for Agricultural Commodity Futures Contracts
494(1)
9.6.3 Conclusion and Prospects
495(1)
References
495(1)
Further Reading
496(1)
Glossary, Quotations and Policy on Websites
497(2)
Chapter 10 Foreign Exchange Markets and Products
499(58)
Antonio Castagna
10.1 The FX Market
499(10)
10.1.1 FX Rates and Spot Contracts
499(1)
10.1.2 Outright and FX Swap Contracts
500(4)
10.1.3 FX Option Contracts
504(3)
10.1.4 Main Traded FX Options Structures
507(2)
10.2 Pricing Models for FX Options
509(2)
10.2.1 The Black-Scholes Model
510(1)
10.3 The Volatility Surface
511(1)
10.4 Barrier Options
512(1)
10.4.1 A Taxonomy of Barrier Options
512(1)
10.5 Sources of FX Risk Exposure
513(4)
10.6 Hedging FX Exposures Embedded in Energy and Commodity Contracts
517(16)
10.6.1 FX Forward Exposures and Conversions
518(4)
10.6.2 FX-Linked Energy Contracts
522(11)
10.7 Typical Hedging Structures for FX Risk Exposure
533(24)
10.7.1 Collar Plain Vanilla
533(3)
10.7.2 Leveraged Forward
536(2)
10.7.3 Participating Forward
538(2)
10.7.4 Knock-Out Forward
540(3)
10.7.5 Knock-In Forward
543(2)
10.7.6 Knock-In Knock-out Forward
545(3)
10.7.7 Resettable Forward
548(2)
10.7.8 Range Resettable Forward
550(3)
References
553(4)
PART TWO Quantitative Topics
Chapter 11 An Introduction to Stochastic Calculus with Matlab® Examples
557(78)
Laura Ballotta
Gianluca Fusai
11.1 Brownian Motion
558(8)
11.1.1 Defining Brownian Motion
558(8)
11.2 The Stochastic Integral and Stochastic Differential Equations
566(9)
11.2.1 Introduction
566(1)
11.2.2 Defining the Stochastic Integral
567(1)
11.2.3 The Ito Stochastic Integral as a Mean Square Limit of Suitable Riemann-Stieltjes Sums
567(1)
11.2.4 A Motivating Example: Computing ∫t 0W(s)dW(s)
568(1)
11.2.5 Properties of the Stochastic Integral
569(2)
11.2.6 Ito Process and Stochastic Differential Equations
571(2)
11.2.7 Solving Stochastic Integrals and/or Stochastic Differential Equations
573(2)
11.3 Introducing Ito's Formula
575(6)
11.3.1 A Fact from Ordinary Calculus
576(1)
11.3.2 Ito's Formula when Y = g(x), g(x) C2
576(1)
11.3.3 Guiding Principle
577(1)
11.3.4 Ito's Formula when Y(t) = g(t, X), g(t, X) C1, 2
577(1)
11.3.5 The Multivariate Ito's Lemma when Z = g(t, X, Y)
578(3)
11.4 Important SDEs
581(37)
11.4.1 The Geometric Brownian Motion GBM(μ, σ)
581(7)
11.4.2 The Vasicek Mean-Reverting Process
588(7)
11.4.3 The Cox-Ingersoll-Ross (CIR) Model
595(9)
11.4.4 The Constant Elasticity of Variance (CEV) Model
604(3)
11.4.5 The Brownian Bridge
607(4)
11.4.6 The Stochastic Volatility Heston Model (1987)
611(7)
11.5 Stochastic Processes with Jumps
618(17)
11.5.1 Preliminaries
618(5)
11.5.2 Jump Diffusion Processes
623(5)
11.5.3 Time-Changed Brownian Motion
628(4)
11.5.4 Final Remark: Levy Processes
632(1)
References
633(1)
Further Reading
633(2)
Chapter 12 Estimating Commodity Term Structure Volatilities
635(24)
Andrea Roncoroni
Rachid Id Brik
Mark Cummins
12.1 Introduction
635(1)
12.2 Model Estimation Using the Kalman Filter
635(11)
12.2.1 Description of the Methodology
636(6)
12.2.2 Case Study: Estimating Parameters on Crude Oil
642(4)
12.3 Principal Components Analysis
646(9)
12.3.1 PCA: Technical Presentation
647(4)
12.3.2 Case Study: Risk Analysis on Energy Markets
651(4)
12.4 Conclusion
655(4)
Appendix
655(2)
References
657(2)
Chapter 13 Nonparametric Estimation of Energy and Commodity Price Processes
659(14)
Gianna Figa-Talamanca
Andrea Roncoroni
13.1 Introduction
659(1)
13.2 Estimation Method
660(3)
13.3 Empirical Results
663(10)
References
672(1)
Chapter 14 How to Build Electricity Forward Curves
673(14)
Ruggero Caldana
Gianluca Fusai
Andrea Roncoroni
14.1 Introduction
673(1)
14.2 Review of the Literature
674(1)
14.3 Electricity Forward Contracts
675(2)
14.4 Smoothing Forward Price Curves
677(2)
14.5 An Illustrative Example: Daily Forward Curve
679(5)
14.6 Conclusion
684(3)
References
684(3)
Chapter 15 GARCH Models for Commodity Markets
687(68)
Eduardo Rossi
Filippo Spazzini
15.1 Introduction
687(3)
15.2 The GARCH Model: General Definition
690(9)
15.2.1 The ARCH(q) Model
692(1)
15.2.2 The GARCH(p, q) Model
693(2)
15.2.3 The Yule-Walker Equations for the Squared Process
695(1)
15.2.4 Stationarity of the GARCH(p, q)
696(2)
15.2.5 Forecasting Volatility with GARCH
698(1)
15.3 The IGARCH(p, q) Model
699(1)
15.4 A Permanent and Transitory Component Model of Volatility
700(2)
15.5 Asymmetric Models
702(5)
15.5.1 The EGARCH(p, q) Model
702(2)
15.5.2 Other Asymmetric Models
704(2)
15.5.3 The News Impact Curve
706(1)
15.6 Periodic GARCH
707(1)
15.6.1 Periodic EGARCH
708(1)
15.7 Nesting Models
708(5)
15.8 Long-Memory GARCH Models
713(7)
15.8.1 The FIGARCH Model
716(3)
15.8.2 The FIEGARCH Model
719(1)
15.9 Estimation
720(2)
15.9.1 Likelihood Computation
720(2)
15.10 Inference
722(3)
15.10.1 Testing for ARCH Effects
722(1)
15.10.2 Test for Asymmetric Effects
723(2)
15.11 Multivariate GARCH
725(2)
15.11.1 BEKK Parameterization of MGARCH
726(1)
15.11.2 The Dynamic Conditional Correlation Model
726(1)
15.12 Empirical Applications
727(13)
15.12.1 Univariate Volatility Modelling
727(6)
15.12.2 A Simple Risk Measurement Application: A Bivariate Example with Copulas
733(7)
15.13 Software
740(15)
References
748(7)
Chapter 16 Pricing Commodity Swaps with Counterparty Credit Risk: The Case of Credit Value Adjustment
755(46)
Marina Marena
Gianluca Fusai
Chiara Quaglini
16.1 Introduction
755(1)
16.1.1 Energy Company Strategies in Derivative Instruments
755(1)
16.2 Company Energy Policy
756(2)
16.2.1 Commodity Risk
756(1)
16.2.2 Credit Risk
757(1)
16.3 A Focus on Commodity Swap Contracts
758(2)
16.3.1 Definition and Main Features of a Commodity Swap
758(2)
16.4 Modelling the Dynamics of Oil Spot Prices and the Forward Curve
760(4)
16.4.1 The Schwartz and Smith Pricing Model
760(4)
16.5 An Empirical Application
764(13)
16.5.1 The Commodity Swap Features
764(1)
16.5.2 Calibration of the Theoretical Schwartz and Smith Forward Curve
765(7)
16.5.3 The Monte Carlo Simulation of Oil Spot Prices
772(1)
16.5.4 The Computation of Brent Forward Curves at Any Given Valuation Date
773(4)
16.6 Measuring Counterparty Risk
777(11)
16.6.1 CVA Calculation
779(3)
16.6.2 Swap Fixed Price Adjustment for Counterparty Risk
782(2)
16.6.3 Right- and Wrong-Way Risk
784(4)
16.7 Sensitivity Analysis
788(1)
16.8 Accounting for Derivatives and Credit Value Adjustments
788(9)
16.8.1 Example of Hedge Effectiveness
791(5)
16.8.2 Accounting for CVA
796(1)
16.9 Conclusions
797(4)
References
798(1)
Further Reading
798(3)
Chapter 17 Pricing Energy Spread Options
801(26)
Fred Espen Benth
Hanna Zdanowicz
17.1 Spread Options in Energy Markets
801(4)
17.2 Pricing of Spread Options with Zero Strike
805(8)
17.3 Issues of hedging
813(2)
17.4 Pricing of Spread Options with Nonzero Strike
815(12)
17.4.1 Kirk's Approximation Formula
817(3)
17.4.2 Approximation by Margrabe Based on Taylor Expansion
820(3)
17.4.3 Other Pricing Methods
823(1)
Acknowledgement
824(1)
References
825(2)
Chapter 18 Asian Options: Payoffs and Pricing Models
827(50)
Gianluca Fusai
Marina Marena
Giovanni Longo
18.1 Payoff Structures
832(1)
18.2 Pricing Asian Options in the Lognormal Setting
833(23)
18.2.1 Moment Matching
835(9)
18.2.2 Lower Price Bound
844(1)
18.2.3 Monte carlo simulation
845(11)
18.3 A Comparison
856(2)
18.4 The Flexible Square-Root Model
858(16)
18.4.1 General Setup
861(9)
18.4.2 Numerical Results
870(1)
18.4.3 A Case Study
871(3)
18.5 Conclusions
874(3)
References
874(3)
Chapter 19 Natural Gas Storage Modelling
877(24)
Alvaro Cartea
James Cheeseman
Sebastian Jaimungal
19.1 Introduction
877(1)
19.2 A Simple Model of Storage, Futures Prices, Spot Prices And Convenience Yield
878(2)
19.3 Valuation of Gas Storage
880(21)
19.3.1 Least-Squares Monte Carlo
881(2)
19.3.2 LSMC Greeks
883(1)
19.3.3 Extending the LSMC to Price Gas Storage
883(1)
19.3.4 Toy Storage Model
884(4)
19.3.5 Storage LSMC
888(2)
19.3.6 Swing Options
890(1)
19.3.7 Closed-Form Storage Solution
891(1)
19.3.8 Monte Carlo Convergence
892(2)
19.3.9 Simulated Storage Operations
894(3)
19.3.10 Storage Value
897(2)
References
899(2)
Chapter 20 Commodity-Linked Arbitrage Strategies and Porttolio Management
901(38)
Viviana Fanelli
20.1 Commodity-Linked Arbitrage Strategies
902(19)
20.1.1 The Efficient Market Hypothesis
902(1)
20.1.2 Risk Arbitrage Opportunities in Commodity Markets
903(3)
20.1.3 Basic Quantitative Trading Strategies
906(8)
20.1.4 A General Statistical Arbitrage Trading Methodology
914(7)
20.2 Portfolio Optimization with Commodities
921(18)
20.2.1 Commodities as an Asset Class
921(2)
20.2.2 Commodity Futures Return Characteristics
923(2)
20.2.3 Risk Premiums in Commodity Markets
925(3)
20.2.4 Commodities as a Portfolio Diversifier
928(1)
20.2.5 Risk-Return Optimization in Commodity Portfolios
929(7)
Symbols
936(1)
References
936(3)
Chapter 21 Econometric Analysis of Energy and Commodity Markets: Multiple Hypothesis Testing Techniques
939(28)
Mark Cummins
21.1 Introduction
939(1)
21.2 Multiple Hypothesis Testing
940(3)
21.2.1 Generalized Familywise Error Rate
941(1)
21.2.2 Per-Familywise Error Rate
942(1)
21.2.3 False Discovery Proportion
942(1)
21.2.4 False Discovery Rate
943(1)
21.2.5 Single-Step and Stepwise Procedures
943(1)
21.3 Energy-Emissions Market Interactions
943(10)
21.3.1 Literature Review
943(1)
21.3.2 Data Description
944(1)
21.3.3 Testing Framework
945(5)
21.3.4 Empirical Results
950(3)
21.4 Emissions Market Interactions
953(3)
21.4.1 Testing Framework and Data
953(2)
21.4.2 Empirical Results
955(1)
21.5 Quantitative Spread Trading in Oil Markets
956(11)
21.5.1 Testing Framework and Data
956(1)
21.5.2 Optimal Statistical Arbitrage Model
957(2)
21.5.3 Resampling-Based MHT Procedures
959(5)
21.5.4 Empirical Results
964(1)
References
964(3)
Appendix A Quick Review of Distributions Relevant in Finance with Matlab® Examples 967(38)
Laura Ballotta
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 PhDs 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. Marks 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 BPs energy and commodity activities. Mark is engaged in ongoing industry training and consultancy activities, focused on the energy sector primarily.
Püsilink: https://www.kriso.ee/db/9781119011590_pe.html