Commodities: Fundamental Theory of Futures, Forwards, and Derivatives Pricing (Chapman and Hall/CRC Financial Mathematics Series)

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
About the Editors
List of Contributors
Introduction
Section 1 Oil Products
	Chapter 1 The Volatility Risk Premium in the Oil Market
		1.1 Introduction
		1.2 Data Description
		1.3 The Smile and Term-Structure of VRP
			1.3.1 VRP Position-Level Analysis
			1.3.2 VRP Portfolio-Level Analysis
		1.4 Delta-Hedging in Practice and Transaction Costs
		1.5 Hedgers Behavior and VRP Regime Changes
		1.6 Conclusions and Further Research
		References
	Chapter 2 Determinants of Oil Futures Prices and Convenience Yields
		2.1 Introduction
		2.2 Oil Price Features
			2.2.1 Term Structure of Oil Futures Open Interest
			2.2.2 Single-Factor Convenience Yield Models
				2.2.2.1 Short Term Pricing Errors
				2.2.2.2 Convenience Yields Inferred from the Schwartz–Smith (2000) Model
				2.2.2.3 Principal Component Analysis on Convenience Yields
		2.3 Three-Factor Model Statement
			2.3.1 Dynamics of Spot Prices
			2.3.2 Two-Factor Convenience Yield
			2.3.3 Results
			2.3.4 Comparison with Two‑Factor Models
		2.4 Model Interpretation
			2.4.1 Explanatory Variable Specification
				2.4.1.1 Financial Variables
				2.4.1.2 Business Cycle Variables
				2.4.1.3 Demand Variables
				2.4.1.4 Trading Variables
			2.4.2 SVAR Model Statement
			2.4.3 Results
				2.4.3.1 x Factor Impulse Responses
				2.4.3.2 y Factor Impulse Responses
				2.4.3.3 p Factor Impulse Responses
				2.4.3.4 Forecast Error Variance Decomposition
				2.4.3.5 Summary
		2.5 Conclusion
		Acknowledgements
		References
		Appendix
			Three-Factor Model in State Space Form
	Chapter 3 Pricing and Hedging of Long-Term Futures and Forward Contracts with a Three-Factor Model
		3.1 Introduction
		3.2 Model
		3.3 Estimation of Parameters
			3.3.1 Estimation Results
				3.3.1.1 NYMEX WTI (Light Sweet Crude Oil)
				3.3.1.2 LME Copper
			3.3.2 Comparison with Actual Data
		3.4 Futures Hedging Techniques
			3.4.1 Hedging Error Rate of the Parallel Hedge
			3.4.2 Hedging Error Rate of the Delta Hedge
		3.5 Stability of the Delta Hedge
			3.5.1 Out of Sample Hedges
			3.5.2 Hedging Long-Term Forward Contracts
		3.6 Measuring the Distribution of Hedging Error Rates
			3.6.1 Distribution of Hedging Error Rates
		3.7 Conclusion
		Acknowledgements
		References
		Appendix A: Expectation and Covariance Matrix of State Variables
		Appendix B: The Number of Futures Units
	Chapter 4 Planning Logistics Operations in the Oil Industry
		4.1 Introduction
		4.2 The Deterministic DROP Model
			4.2.1 Mathematical Formulation of the DROP Model
			4.2.2 Objective Function
			4.2.3 Constraints
				4.2.3.1 Operational Constraints
				4.2.3.2 Product Balance Constraints
				4.2.3.3 Capacity Constraints
				4.2.3.4 Software Implementation
		4.3 Stochastic Modelling
			4.3.1 The Stochastic DROP Model
			4.3.2 Industry Data Analysis and Simulation
			4.3.3 Stochastic Model Implementation
			4.3.4 Analysis of Stochastic Problem Solutions
		4.4 Conclusions and Future Directions
		Acknowledgements
		References
		Appendix
			Sets
			Parameters
			Variables
			Bounds
	Chapter 5 Analysing the Dynamics of the Refining Margin: Implications for Valuation and Hedging
		5.1 Introduction
		5.2 Data and Preliminary Findings
			5.2.1 Data Description
			5.2.2 Preliminary Findings About the Refining Margin
		5.3 Common Long-Term Trend Factor Models
			5.3.1 Theoretical Models
			5.3.2 Estimation Results
		5.4 Crack-Spread Option Valuation
			5.4.1 Data
			5.4.2 Option Valuation Methodology
			5.4.3 Commodity Price Dynamics
			5.4.4 Results
		5.5 Conclusion
		References
		Appendix A: Estimation Methodology
		Appendix B: Modelling the Crack-Spread Directly
	Chapter 6 Long-Term Spread Option Valuation and Hedging
		6.1 Introduction
		6.2 Cointegrated Prices and Mean Reversion of the Spread
			6.2.1 Cointegration Tests
			6.2.2 Risk Neutral and Market Measure Spread Mean Reversion Tests
		6.3 Modelling the Spread Process
			6.3.1 Spread Process in the Risk Neutral Measure
			6.3.2 Spread Process in the Market Measure
			6.3.3 Futures Pricing
			6.3.4 Calibration
		6.4 Spread Option Pricing and Hedging
		6.5 Crack Spread: Heating Oil/WTI Crude Oil
			6.5.1 Data
			6.5.2 Unit Root and Cointegration Tests
			6.5.3 Model Calibration
			6.5.4 Futures Spread Option Valuation
		6.6 Location Spread: Brent/WTI Crude Oil
			6.6.1 Data
			6.6.2 Unit Root and Cointegration Tests
			6.6.3 Model Calibration
			6.6.4 Futures Spread Option Valuation
		6.7 Conclusion
		Acknowledgements
		References
		Appendix
			Two Price Method of Simulating Spreads
Section 2 Other Commodities
	Chapter 7 A Rough Multi-Factor Model of Electricity Spot Prices
		7.1 Introduction
		7.2 The Data
		7.3 Roughness in Electricity Prices
		7.4 A Rough Multi-Factor Modelling Framework
			7.4.1 Modelling the Seasonal Component
			7.4.2 Modelling the Base Component
				7.4.2.1 Models with and without Characterisic Time Scale in Their Autocorrelations
				7.4.2.2 Stochastic Volatility: Marginal Distribution of the Base Component
				7.4.2.3 An Alternative Rough Component
			7.4.3 Modelling the Spike Component
			7.4.4 Estimation of the Rough Multi-Factor Model
				7.4.4.1 Estimating the Roughness Index of Time Series of Electricity Prices
				7.4.4.2 Estimating the Mean Reversion Parameter λ
		7.5 Implications of a Rough Model of Electricity Prices
			7.5.1 Arbitrage
			7.5.2 Forecasting
				7.5.2.1 Forecasting the BSS Process
				7.5.2.2 Forecasting the fBm-OU Process
		7.6 Empirical Analysis
			7.6.1 Roughness of Electricity Price Series
			7.6.2 Characteristic Time Scale of Electricity Price Series
			7.6.3 Marginal Distribution of the Base Signal
			7.6.4 Forecasting
		7.7 Conclusion
		Acknowledgements
		References
		Appendix A: An Empirical Case Study: The German EEX Market
			A.1 De-seasonalizing the Data
			A.2 Filtering the Jump Process
			A.3 Analysis of the Residual Base Signal
			A.4 Forecasting the Base Signal
		Appendix B: More Estimators of the Roughness Index
		Appendix C: Estimating the Degree of Roughness in Practice
			C.1 Roughness Estimation in the Presence of Spikes
			C.2 Roughness Estimation using Low-Frequency Data
			C.3 Lessons from the Simulation Experiment
	Chapter 8 Investing in the Wine Market: A Country-Level Threshold Cointegration Approach
		8.1 Introduction
		8.2 Wine: The Theoretical Framework
		8.3 The Mediobanca Index: Evidence on Stock Performance
			8.3.1 The Data
			8.3.2 Performance
		8.4 Econometric Methodology
		8.5 Empirical Results
		8.6 Conclusion
		References
	Chapter 9 Cross-Market Soybean Futures Price Discovery: Does the Dalian Commodity Exchange Affect the Chicago Board of Trade?
		9.1 Introduction
		9.2 The Data
			9.2.1 The Construction of Continuous Price Series
			9.2.2 Calculation of Futures Returns
		9.3 The SVAR Model
			9.3.1 Model Setup
			9.3.2 Estimation Results
			9.3.3 Impulse Response Analysis
			9.3.4 Forecasted Error Variance Decomposition
		9.4 The VEC Model
			9.4.1 Model Setup
			9.4.2 Estimation Results
			9.4.3 Impulse Response and FEVD
		9.5 Conclusion
		Acknowledgements
		References
	Chapter 10 The Structure of Gold and Silver Spread Returns
		10.1 Introduction
		10.2 Data
		10.3 Rescaled Adjusted Range Analysis
		10.4 Trading Implications
		10.5 Conclusions
		References
	Chapter 11 Gold and the U.S. Dollar: Tales from the Turmoil
		11.1 Introduction
		11.2 The Dataset
		11.3 Some Structural Evidence on Volatility Spillovers
		11.4 A Look at the Tails
		11.5 Conclusion
		Acknowledgements
		References
	Chapter 12 Application of a TGARCH-Wavelet Neural Network to Arbitrage Trading in the Metal Futures Market in China
		12.1 Introduction
		12.2 Model Development: TGARCH-WNN Statistical Arbitrage
			12.2.1 Mean Spread with TGARCH
			12.2.2 Wavelet Theory and Chaotic Properties of Time Series
			12.2.3 Wavelet Neural Networks
		12.3 Sample Selection
		12.4 Empirical Study
			12.4.1 Application of the TGARCH Model
			12.4.2 Testing Chaotic Properties Using Lyapunov Exponents
			12.4.3 Robustness Check of the TGARCH-WNN, Backpropagation Neural Network and Historical Optimal Methods
				12.4.3.1 Comparison of Prediction Accuracy
				12.4.3.2 Comparison of Rate of Return under Different Circumstances
				12.4.3.3 The Impact of Commission
		12.5 Conclusion
		Acknowledgements
		References
	Chapter 13 Multivariate Continuous-Time Modeling of Wind Indexes and Hedging of Wind Risk
		13.1 Introduction
		13.2 Data Presentation
		13.3 Model Description
			13.3.1 General Model Considerations
			13.3.2 A Gamma Model
				13.3.2.1 Distribution of Pn(t) in the Gamma Model
				13.3.2.2 Covariance between Wind Indexes in the Gamma Model
				13.3.2.3 Identification of Parameters in the Gamma Model
			13.3.3 A Lognormal Model
				13.3.3.1 Distribution of Pi(t) in the Lognormal Model
				13.3.3.2 Covariance between Wind Indexes in the Lognormal Model
				13.3.3.3 Identification of Parameters in the Lognormal Model
			13.3.4 Comparison of the Gamma and Lognormal Model
		13.4 Estimation Results
			13.4.1 Gamma Model
			13.4.2 Lognormal Model
		13.5 Hedging Wind Power Production
			13.5.1 Perfect Hedging of Volumetric Risk Using Tailor-Made Wind Power Futures
			13.5.2 Minimum Variance Hedge of a Tailor-Made WPF Contracts Portfolio
				13.5.2.1 In-Sample Hedging Effectiveness
				13.5.2.2 Out-of-Sample Hedging Effectiveness
				13.5.2.3 Risk Premium of Wind Power Futures
		13.6 Conclusion
		Acknowledgment
		Disclosure Statement
		Funding
		References
		Appendix: Theoretical Results for the Gamma Model
Section 3 Commodity Prices and Financial Markets
	Chapter 14 Short-Horizon Return Predictability and Oil Prices
		14.1 Introduction
		14.2 Oil Price, the Business Cycle and Stock Returns
			14.2.1 Oil Price and the Macroeconomy
			14.2.2 Oil Price and the Financial Market
			14.2.3 Stock Returns and the Business Cycle
			14.2.4 Measuring Oil Price Shocks
			14.2.5 Oil Price, the Business Cycle and Stock Returns
		14.3 Short-Horizon Predictability of Stock Returns
		14.4 Out-of-Sample Predictability of Stock Returns
		14.5 Long-Horizon Predictability of Stock Returns
		14.6 Implications for the Cross Section of Expected Returns
		14.7 Conclusions
		Acknowledgements
		References
		Appendix: Tests for Out-of-Sample Predictability
	Chapter 15 Time-Frequency Analysis of Crude Oil and S&P 500 Futures Contracts
		15.1 Introduction
		15.2 Literature Review
		15.3 Data
		15.4 Methodology
			15.4.1 Cross-Spectral Analysis
			15.4.2 Wavelets
			15.4.3 Cross Wavelets
			15.4.4 Cross Wavelet Phase Angles
		15.5 Empirical Results
			15.5.1 Cospectral Densities
			15.5.2 Wavelets
		15.6 Conclusions
		Acknowledgements
			Software Acknowledgements
		References
	Chapter 16 Sectoral Stock Return Sensitivity to Oil Price Changes: A Double-Threshold FIGARCH Model
		16.1 Introduction
		16.2 Data and Methodology
			16.2.1 Data Description
			16.2.2 Methodology and Models
				16.2.2.1 The Return and Conditional Volatility Generating Processes
				16.2.2.2 The Estimated Model
			16.2.3 Testable Hypotheses
		16.3 Empirical Findings
			16.3.1 Tests of Model Specification
			16.3.2 Tests of Asymmetry
			16.3.3 Sector Analysis
				16.3.3.1 Analysis of Oil Effects (Roil Coefficient) Below and Above the Threshold
				16.3.3.2 Summary of Sector Returns
				16.3.3.3 Economic Significance
			16.3.4 The FIGARCH Volatility Equation
		16.4 Conclusions
		Acknowledgements
		References
		Appendix: Threshold and Delay Parameter Values
	Chapter 17 Long–Short Versus Long-Only Commodity Funds
		17.1 Introduction
		17.2 Sources of Alpha in Commodity Markets
		17.3 Temporal Performance Measures
		17.4 A Relative-Value Commodity Approach
		17.5 Conclusions
		References
	Chapter 18 The Dynamics of Commodity Prices
		18.1 Introduction
		18.2 Models and Estimation
			18.2.1 Spot Price Models
			18.2.2 Estimation Approach
		18.3 Data and Empirical Results
			18.3.1 Data
			18.3.2 Univariate Analysis
			18.3.3 Cross-Commodity Market Analysis
			18.3.4 Commodity and Equity Markets
		18.4 Economic Implications
			18.4.1 Options Valuation
			18.4.2 Options Hedging
		18.5 Conclusions
		Acknowledgements
		References
		Appendix: MCMC Estimation Details
	Chapter 19 Short-Term and Long-Term Dependencies of the S&P 500 Index and Commodity Prices
		19.1 Introduction
		19.2 Brief Literature Review
		19.3 Measuring Stock and Commodities Market Dependencies Using Wavelet Squared Coherency
		19.4 Data
			19.4.1 Commodity and Stock Indexes
			19.4.2 Data Description
		19.5 Empirical Analysis of Comovements
		19.6 Conclusion
		Acknowledgements
		References
	Chapter 20 Commodity Markets through the Business Cycle
		20.1 Introduction
		20.2 The Reaction of Commodity Markets to Economic News
			20.2.1 Measuring the Impact of Price Discovery on Asset Prices
			20.2.2 Database of News
			20.2.3 An Example: S&P 500, US 10-Year and USD
			20.2.4 Commodity by Commodity Analysis
				20.2.4.1 Database for Commodity Prices
				20.2.4.2 Precious Metals
				20.2.4.3 Industrial Metals
				20.2.4.4 Energy
				20.2.4.5 Agricultural Commodities
			20.2.5 Rolling Analysis
		20.3 Economic Regimes and Commodity Markets as an Asset Class
			20.3.1 Measuring the Business Cycle
			20.3.2 To Which Business Cycle Are the Commodity Markets Related?
			20.3.3 Commodity Performances Depending on the Nature of Each Economic Regime
		20.4 Conclusion
		References
		Appendix: Database of News
	Chapter 21 A Hybrid Commodity and Interest Rate Market Model
		21.1 Introduction
		21.2 The Commodity LIBOR Market Model
			21.2.1 The Interest Rate Market
			21.2.2 The Commodity Market
		21.3 Calibration with Time-Dependent Volatilities
		21.4 Futures/Forward Relation and Convexity Correction
		21.5 Merging Interest Rate and Commodity Calibrations
		21.6 Real Data Example
		21.7 Pricing Spread Options
		21.8 Conclusion
		Acknowledgements
		References
		Appendix: Volatility Integrals
	Chapter 22 Evaluation of Gas Sales Agreements with Indexation Using Tree and Least-Squares Monte Carlo Methods on Graphics Processing Units
		22.1 Introduction
		22.2 The Pricing Framework and the Indexation Principle
		22.3 Gas Sales Agreements with Indexation
		22.4 The Evaluation Using Trinomial Trees
			22.4.1 The Structure of a Trinomial Tree
			22.4.2 Pricing Algorithm
		22.5 The Evaluation Using LSMC
		22.6 Evaluation on GPUs
			22.6.1 General-Purpose Computing on GPUs
			22.6.2 LSMC on GPUs
		22.7 Numerical Examples
			22.7.1 Contract Values
			22.7.2 The LSMC Algorithm on the GPU
			22.7.3 Accuracy of the Tree Algorithm
			22.7.4 Value Surfaces and Decision Surfaces from the Tree Algorithm
			22.7.5 Contract Values with Respect to Parameters
			22.7.6 Self-Indexed Swing Options
		22.8 Conclusion
		Acknowledgements
		Disclosure Statement
		ORCID
		References
		Appendix A: Construction of the Two-Dimensional Trinomial Tree
		Appendix B: Notes on the Implementation of the Tree Algorithm on GPUs
Section 4 Electricity Markets
	Chapter 23 Modeling the Distribution of Day-Ahead Electricity Returns: A Comparison
		23.1 Introduction
		23.2 Data and Preliminary Analysis
		23.3 Distributions of Electricity Returns
		23.4 Fitting the Empirical Probability Densities
		23.5 Robustness
		23.6 Conclusion
		References
	Chapter 24 Stochastic Spot Price Multi-Period Model and Option Valuation for Electrical Markets
		24.1 Introduction
		24.2 Spot Price Multi-Period Model
		24.3 Model Calibration
			24.3.1 The Price Forward Curve
			24.3.2 The Short-Term Process
			24.3.3 The Long-Term Process
			24.3.4 The Term Structure of Volatility
		24.4 Scenario Generation and Valuation of Structured Products
			24.4.1 Estimation of the Volatility Surface
		24.5 Structured Product Valuation
			24.5.1 Valuation and Risk Analysis: TS-Energy
			24.5.2 Virtual Power Plant Valuation
		24.6 Conclusion
		Acknowledgements
		References
	Chapter 25 Modelling Spikes and Pricing Swing Options in Electricity Markets
		25.1 Introduction
		25.2 Properties of the Model for Spot Prices
			25.2.1 The Spike Process
			25.2.2 The Combined Process
			25.2.3 Approximations
		25.3 Option Pricing
			25.3.1 Pricing Path-Independent Options
			25.3.2 Pricing Options on Forwards
			25.3.3 Pricing Options on Forwards with a Delivery Period
		25.4 Pricing Swing Options
			25.4.1 The Grid Approach
			25.4.2 Numerical Results
		References
	Chapter 26 Efficient Pricing of Swing Options in Lévy-Driven Models
		26.1 Introduction
		26.2 Lévy Processes: Basic Facts
			26.2.1 General Definitions
			26.2.2 Regular Lévy Processes of Exponential Type
			26.2.3 The Wiener−Hopf Factorization
		26.3 The Multiple Optimal Stopping Problem for Swing Options
		26.4 The Finite Difference Scheme for Pricing Swing Options
		26.5 Pricing Swing Options Using the Wiener−Hopf Approach
		26.6 Numerical Results
		Acknowledgements
		References
	Chapter 27 The Valuation of Clean Spread Options: Linking Electricity, Emissions and Fuels
		27.1 Introduction
		27.2 The Bid Stack: Price Setting in Electricity Markets
			Assumption 27.2.1
		27.3 Risk-Neutral Pricing of Allowance Certificates
			27.3.1 The Market Emission Rate
			27.3.2 The Pricing Problem
			27.3.3 An FBSDE for the Allowance Price
			Assumption 27.3.2
			27.3.4 Existence of a Solution to the Pricing Problem
		27.4 Valuing Clean Spread Options
		27.5 A Concrete Two-Fuel Model
			27.5.1 The Bid Stack
			27.5.2 The Emission Stack
			27.5.3 Specifying the Exogenous Stochastic Factors
		27.6 Numerical Analysis
			27.6.1 Choice of Parameters
			27.6.2 Case Study I: Impact of the Emission Market
			27.6.3 Case Study II: Impact of Fuel Price Changes
			27.6.4 Case Study III: Comparison with Reduced-Form
			27.6.5 Case Study IV: Cap-and-Trade vs. Carbon Tax
		27.7 Conclusion
		Acknowledgements
		References
		Appendix A: Numerical Solution of the FBSDE
			A.1 Candidate Pricing PDE
			A.2 An Implicit–Explicit Finite Difference Scheme
		Appendix B: Numerical Calculation of Spread Prices
			B.1 Time Discretization of SDEs
			B.2 Monte Carlo Calculation of Option Prices
	Chapter 28 Is the EUA a New Asset Class?
		28.1 Introduction
		28.2 Stylised Facts of Asset Returns
		28.3 Data
		28.4 Results
			28.4.1 Data Distribution
			28.4.2 Correlation Facts
			28.4.3 Volatility-Related Features
			28.4.4 Commodity Features
		28.5 Conclusions
		Acknowledgements
		References
Section 5 Contemporary Topics
	Chapter 29 Volatility Is Rough
		29.1 Introduction
			29.1.1 Volatility Modeling
			29.1.2 Fractional Volatility
			29.1.3 The Shape of the Implied Volatility Surface
			29.1.4 Main Results and Organization of the Paper
		29.2 Smoothness of the Volatility: Empirical Results
			29.2.1 Estimating the Smoothness of the Volatility Process
			29.2.2 DAX and Bund Futures Contracts
			29.2.3 S&P and NASDAQ Indices
			29.2.4 Other Indices
			29.2.5 Distribution of the Increments of the Log-Volatility
			29.2.6 Does H Vary Over Time?
		29.3 A Simple Model Compatible with the Empirical Scaling of the Volatility
			29.3.1 Specification of the RFSV Model
				29.3.1.1 RFSV vs. FSV
			29.3.2 RFSV Model Autocovariance Functions
			29.3.3 RFSV vs. FSV Again
			29.3.4 Simulation-Based Analysis of the RFSV Model
		29.4 Spurious Long Memory of Volatility?
		29.5 Forecasting Using the RFSV Model
			29.5.1 Forecasting Log-Volatility
			29.5.2 Variance Prediction
		29.6 Conclusion
		Acknowledgements
		Disclosure Statement
		Funding
		References
		Appendix A: Proofs
			A.1 Proof of Proposition 29.3.1
			A.2 Proof of Corollary 29.3.1
		Appendix B: Estimations of H
			B.1 On Different Indices
			B.2 On Different Time Intervals
		Appendix C: The Effect of Smoothing
			C.1 Numerical Example
	Chapter 30 Algorithmic Trading in a Microstructural Limit Order Book Model
		30.1 Introduction
		30.2 Model Setup
			30.2.1 Order Book Representation
			30.2.2 Market Maker Strategies
				30.2.2.1 Control of the Market Maker
				30.2.2.2 Controlled Order Book
		30.3 Presentation of the Market-Making Problem. Theoretical Resolution
			30.3.1 Definition of the Market-Making Problem and Well-Posedness of the Value Function
			30.3.2 Markov Decision Process Formulation of the Market-Making Problem
			30.3.3 Proof of Theorem 30.3.1
		30.4 Numerical Resolution of the Market-Making Control Problem
			30.4.1 Framework
			30.4.2 Presentation and Rate of Convergence of the Qknn Algorithm
			30.4.3 Qknn Algorithm Applied to the Order Book Control Problem (30.1)
				30.4.3.1 Training Set Design
			30.4.4 Numerical Results
				30.4.4.1 Case 1: The Market Maker only Place Orders at the Best Ask and Best Bid
				30.4.4.2 Case 2: The Market Maker Place Orders on the First Two Limits of the Orders Book
		30.5 Model Extension to Hawkes Processes
		30.6 Conclusion
		Acknowledgments
		Disclosure Statement
		ORCID
		References
		Appendices
			Appendix A: Proof of Theorem 30.4.1 and Corollary 30.4.1
			Appendix B: Zador’s Theorem
	Chapter 31 Cryptocurrency Liquidity During Extreme Price Movements: Is There a Problem with Virtual Money?
		31.1 Introduction
		31.2 Cryptocurrency Data Description
			31.2.1 Cryptocurrency Data Management
		31.3 Empirical Results
			31.3.1 The Relationship between Cryptocurrency Liquidity and EPM
			31.3.2 The Implications of Cryptocurrency Traders’ Activity on EPM
			31.3.3 The Magnitude of EPM
			31.3.4 The Activity of CTs during Normal Trading and in EPM
			31.3.5 Herding Behaviour during EPM
			31.3.6 Profitability of CTs during EPM
			31.3.7 CTs Activity during Future EPM
		31.4 Robustness Checks
		31.5 Conclusion
		Disclosure Statement
		References
		Appendix
			Cryptocurrency Description
	Chapter 32 Identifying the Influential Factors of Commodity Futures Prices through a New Text Mining Approach
		32.1 Introduction
		32.2 Literature Review
			32.2.1 Influential Factors of Commodity Futures Markets
			32.2.2 Topic Models in Financial Text Mining
		32.3 Proposed DP-Sent-LDA Model
			32.3.1 Sent-LDA
			32.3.2 DP-Sent-LDA
				32.3.2.1 Concept of DP-Sent-LDA
				32.3.2.2 Generative Process of DP-Sent-LDA
				32.3.2.3 Learning Algorithm of DP-sent-LDA
		32.4 Data
		32.5 Empirical Analysis
			32.5.1 Implementation of DP-Sent-LDA
			32.5.2 Comparison of Sent-LDA and DP-Sent-LDA on Predictive Power
			32.5.3 The Identified Influential Factors of Commodity Futures Prices
				32.5.3.1 The Influential Factor System of Commodity Futures Prices
				32.5.3.2 The Relative Importance of the Influential Factors
			32.5.4 Regression Analysis of the Effectiveness of the Influential Factors
				32.5.4.1 Proxy Variable Selection Results
				32.5.4.2 Regression Analysis Results
		32.6 Conclusions
		Disclosure Statement
		Funding
		ORCID
		References
	Chapter 33 Classification of Flash Crashes Using the Hawkes (p,q) Framework*
		33.1 Introduction
		33.2 Data Sets
			33.2.1 The 10-Year U.S. Treasury Note Futures Contract
			33.2.2 The E-Mini S&P500 Futures Contract
			33.2.3 Foreign Exchange Spot Interbank Market
			33.2.4 The Ethereum Cryptocurrency Market
			33.2.5 Sample Selection and Summary
		33.3 The Hawkes Model with Time-Dependent Endogenous Dynamics
		33.4 Calibration to Mid-Price Data—the Hawkes(p,q) Model
		33.5 Endogeneity of Financial Markets around Flash Crashes
			33.5.1 Preprocessing of Timestamps and Calibration Specifics
			33.5.2 The E-Mini S&P 500 Futures Crash in May 2010
			33.5.3 The U.S. Treasury Bond Yield Crash in October 2014
			33.5.4 The US-Dollar Crash in March 2015
			33.5.5 The New Zealand Dollar Crash in August 2015
			33.5.6 The South African Rand Crash in January 2016
			33.5.7 The British Pound Crash in October 2016
			33.5.8 The Ether Crash in June 2017
			33.5.9 The EUR/USD Crash in December 2017
			33.5.10 The USD/JPY Crash in January 2019
		33.6 Residual Analysis and Refined Treatment of Exogenous Clustering
			33.6.1 Residual Analysis
			33.6.2 Comparison to Alternative Models
		33.7 Conclusion
		Disclosure Statement
		References
		Appendices
			Appendix A: The EM Algorithm for the Estimation of the Hawkes(p,q) Process
			Appendix B: Estimating Transient Criticality—A Simulation Study
			Appendix C: The MCEM Algorithm for the Estimation of the MA-Hawkes(p,q) Process
Epilogue