Quantitative Energy Finance: Recent Trends and Developments

✍ Scribed by Fred Espen Benth (editor), Almut E. D. Veraart (editor)

• Publisher: Springer
• Year: 2024
• Tongue: English
• Leaves: 270
• Category: Library

✦ Synopsis

Power markets are undergoing a major transformation from gas and oil-fueled generation toward renewable electricity production from wind and solar sources. Simultaneously, there is an increasing demand for electrification, coupled with long-term climate-induced weather changes. The uncertainties confronting energy market participants require sophisticated modelling techniques to effectively understand risk, many of which are covered in this book.
Comprising invited papers by high-profile researchers, this volume examines the empirical aspects of forward and futures prices, uncovering patterns of noise factors in various European electricity markets. Additionally, it delves into the recent, influential classes of Hawkes and trawl processes, emphasizing their significance in energy markets. The impact of renewables on energy market prices is a pivotal concern for both producers and consumers. Mean-field games provide a powerful mathematical framework for this, and a dedicated chapter outlining their dynamics is included in the book. The book also explores structural financial products and their connection to climate risk as a risk management tool, underscoring the essential need for a comprehensive understanding of these products in the realm of "green finance," to which the energy industry is integral. Lastly, the book thoroughly analyzes spatial smoothing and power purchase (PPA) contracts, addressing central issues in energy system planning and financial operations.
Tailored for researchers, PhD students, and industry energy analysts, this volume equips readers with insights and tools to navigate the constantly evolving energy market landscape. It serves as a sequel to the earlier Quantitative Energy Finance book, featuring all-new chapters.

✦ Table of Contents

Preface
Contents
List of Contributors
Part I Modelling of Energy Prices
Estimation of the Number of Factors in a Multi-Factorial Heath-Jarrow-Morton Model in Power Markets
1 Introduction
2 Model Description
3 Estimation
3.1 Distributions of the Changes in Futures and Spot Prices
3.2 State-Space System of Equations
3.2.1 Elements of the Space Equation
3.2.2 Elements of the State Equation
3.3 Implementation of the Kalman Filter and Minimization Algorithm
3.4 Criteria: AIC and BIC
4 Estimation Results
4.1 Data and Preprocessing Description
4.1.1 Description of the Data
4.1.2 Preprocessing of Data
4.1.3 Seasonal Adjustment of Spot Prices
4.2 Results
5 Conclusion
Appendix: Estimation Results
Belgian Power Market Data
French Power Market Data
German Power Market Data
References
Hawkes Processes in Energy Markets: Modelling, Estimation and Derivatives Pricing
1 Introduction
2 Empirical Evidence of Jump Clusters
3 Literature Review: Hawkes Processes in Energy Markets
4 Hawkes Processes
4.1 Definition Based on the Conditional Intensity
4.2 Moments of λT and NT
5 Simulation of Hawkes Processes with Exponential Kernel
5.1 Euler Scheme
5.2 Ogata's Modified Thinning Algorithm and a Variant
5.3 Dassios and Zhao's Exact Simulation
5.4 Comparison of Exact Methodologies
5.5 Comparison Between Algorithm 4 and the Euler Scheme
6 An Asset Pricing Model with Self-exciting Jumps
6.1 Dynamics Under the Historical Measure
6.2 Dynamics Under the Risk–Neutral Measure
7 Model Estimation
8 Model Simulation and Exotic Derivatives Pricing
8.1 Asian Options
8.2 Exact Simulation of (18)–(19)
8.3 Pricing Exotic Derivatives via Monte Carlo Simulation
9 Concluding Remarks
References
Periodic Trawl Processes: Simulation, Statistical Inference and Applications in Energy Markets
1 Introduction
1.1 Outline and Main Contributions of the Article
2 Mixed Moving Average and Periodic Trawl Processes
2.1 Background
2.2 Definition of a Mixed Moving Average Process
2.3 Periodic Trawl Processes
2.3.1 Second-Order Properties of Periodic Trawl Processes
2.3.2 Examples
3 Simulation of Periodic Trawl Processes
3.1 Slice-Based Simulation for Trawl Processes
3.1.1 Computing the Matrix of Slices
3.1.2 Adding the Weighted Slices
3.1.3 Simulating a Periodic Trawl Process
3.2 A Note on Stochastic Versus Deterministic Seasonality
4 Asymptotic Theory for MMAPs
4.1 Asymptotic Normality of the Sample Mean
4.2 Asymptotic Normality of the Sample Autocovariance
5 Inference for Periodic Trawl Processes Using Methods of Moments
5.1 Exponential Trawl Function
5.2 SupGamma Trawl Function
6 Inference for Periodic Trawl Processes Using a Generalised-Method-of-Moments Approach
6.1 Weak Dependence
6.2 GMM Estimation for Periodic Trawl Processes
7 Empirical Illustration on Electricity Day-Ahead Prices
8 Conclusion and Outlook
Appendix
Proof of the Second Order Properties
Proofs of the Asymptotic Theory
Examples
Exponential Trawl
SupGamma Trawl
Verifying the Assumptions of Theorem 2 for Selected Periodic Trawl Processes
Verifying Condition (11) from Proposition 7
Verifying Assumption (15) from Theorem 2
References
Part II Energy Transition
Fuelling the Energy Transition: The Effect of German Wind and PV Electricity Infeed on TTF Gas Prices
1 Introduction
1.1 European Natural Gas Consumption
1.2 Gas Key Player in the Energy Transition
1.3 Gas Pricing Factors
2 Literature Review
3 Data
4 Methodology
4.1 Baseline Model OLS
4.2 Threshold Regression Model
5 Empirical Results and Discussion
5.1 OLS
5.2 Threshold Regression Models
5.2.1 Wind Infeed Variables as Thresholds
5.2.2 PV Infeed Variables as Thresholds
6 Conclusion
7 Further Research
Appendix
References
A Mean-Field Game Model of Electricity Market Dynamics
1 Introduction
1.1 Literature Review
1.1.1 Optimization Models
1.1.2 Market Equilibrium Models
1.1.3 Simulation Models
1.1.4 Entry and Exit Models for Electricity Market
1.1.5 Mean-Field Games and Their Applications to Economic and Financial Modeling
2 The Model
2.1 Power Plants
2.2 Conventional Producers
2.3 Renewable Producers
2.4 Price Formation
3 Entry and Exit Mean-Field Game
3.1 Definition of the Mean-Field Setting
3.2 Price Formation
3.3 Optimization Functionals
3.4 Definition and Properties of Nash Equilibrium
4 Numerical Resolution and Illustrations
4.1 Numerical Illustration
Appendix: Proof of the Main Results
Preliminary Lemmas
Proof of Theorem 1
Proof of Proposition 2
References
PPA Investments of Minimal Variability
1 Introduction
2 An Infinite-Dimensional Problem
3 A Finite-Dimensional Problem
4 A Case Study with Solar
References
Part III Climate Risk
Climate Risk in Structural Credit Models
1 Introduction
2 Climate Risk
2.1 Physical Risk
2.2 Transition Risk
3 Structural Credit Models
3.1 Merton Model
3.2 Extensions
3.2.1 Jump-Diffusion Models
3.2.2 Optimal Capital Structure Models
4 Climate-Adjusted Models of Credit Risk
4.1 Growth Adjustment
4.2 Shocking the Value Process
4.3 Discontinuous Climate Impacts
4.4 Climate Scenario Uncertainty
5 Conclusions
References