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Quantitative Energy Finance

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About this book

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

Frontmatter

Modelling of Energy Prices

Frontmatter
Estimation of the Number of Factors in a Multi-Factorial Heath-Jarrow-Morton Model in Power Markets
Abstract
We study the calibration of specific multi-factorial Heath-Jarrow-Morton models to power market prices, with a focus on the estimation of the optimal number of Gaussian factors. We describe a common statistical procedure based on likelihood maximisation and Akaike/Bayesian information criteria, in the case of a joint calibration on both spot and futures prices. We perform a detailed analysis on three national markets within Europe: Belgium, France, and Germany. The results show a lot of similarities among all the markets we consider, especially on the optimal number of factors and on the behaviour of the different factors.
Olivier Féron, Pierre Gruet
Hawkes Processes in Energy Markets: Modelling, Estimation and Derivatives Pricing
Abstract
The purpose of the present contribution is to illustrate the extensive use of Hawkes processes in modeling price dynamics in energy markets and to show how they can be applied for derivatives pricing. After a review of the literature devoted to the subject and on the exact simulation of Hawkes processes, we introduce a simple, yet useful, Hawkes-based model for energy spot prices. We present the model under the historical measure and illustrate a structure preserving change of measure, allowing to specify a risk-neutral dynamics. Then, we propose an effective estimation methodology based on particle filtering. Finally, we show how to perform exotic derivatives pricing both through exact simulation and characteristic function inversion techniques.
Riccardo Brignone, Luca Gonzato, Carlo Sgarra
Periodic Trawl Processes: Simulation, Statistical Inference and Applications in Energy Markets
Abstract
This article introduces the class of periodic trawl processes, which are continuous-time, infinitely divisible, stationary stochastic processes, that allow for periodicity and flexible forms of their serial correlation, including both short- and long-memory settings. We derive some of the key probabilistic properties of periodic trawl processes and present relevant examples. Moreover, we show how such processes can be simulated and establish the asymptotic theory for their sample mean and sample autocovariances. Consequently, we prove the asymptotic normality of a (generalised) method-of-moments estimator for the model parameters. We illustrate the new model and estimation methodology in an application to electricity prices.
Almut E. D. Veraart

Energy Transition

Frontmatter
Fuelling the Energy Transition: The Effect of German Wind and PV Electricity Infeed on TTF Gas Prices
Abstract
Previous research shows that renewable energies have a direct negative marginal effect on electricity prices. Gas plants play an essential role in the electricity generation in several fuel-based energy systems through balancing out intermittent renewable energies, which is why it is labeled “green” in the EU Taxonomy. We show the substitution effect between renewable energies, wind and PV, and gas, in the context of a threshold model. Applied to daily Dutch natural gas prices (TTF) between 2016 and 2020, we determine the effect of demand/supply price drivers and lay special emphasis on the asymmetric effects of the day-ahead forecasts of wind and PV infeed. Results show a negative marginal effect of the day-ahead wind and PV infeed forecasts on day-ahead natural gas prices. Employing threshold models we find that in regimes with low wind infeed, marginal increases in the wind and PV infeed forecasts decrease gas prices faster than in regimes with high infeed. Our findings further reveal that the day-ahead TTF price is positively associated with heating demand, supplier concentration, coal, and CO\({ }_2\) prices. We discuss these findings in the context of the debate on the usage of gas for the European energy transition.
Christoph Halser, Florentina Paraschiv
A Mean-Field Game Model of Electricity Market Dynamics
Abstract
We develop a model for the long-term dynamics of electricity market, based on mean-field games of optimal stopping. Our paper extends the recent contribution (Aïd et al., J. Dyn. Games 8(4):331, 2021) in several ways, making the model much more realistic, especially for describing the medium-term impacts of energy transition on electricity markets. In particular, we allow for an arbitrary number of technologies with endogenous fuel prices, introduce plant construction time and enable the agents to both invest and divest. This makes it possible to describe the role of gas generation as a medium-term substitute for coal, to be replaced by renewable generation in the long term, and enables us to model the events like the 2022 energy price crisis.
Alicia Bassière, Roxana Dumitrescu, Peter Tankov
PPA Investments of Minimal Variability
Abstract
We analyse how to use power purchase agreements (PPA) as a spatial hedge to reduce the variability of production less demand from intermittent power sources such as wind and solar. An idealised continuous spatial hedging problem is used as benchmark, providing the minimal possible variability that can be achieved by spreading production locations geographically. It is demonstrated that the variability is reduced with the number of locations included in the portfolio. The analysis rests on modelling capacity factors, which describes the possible production from a power plant of 1MW installed capacity of renewable solar or wind, as a square-integrable random field in Hilbert space with an associated covariance operator. A case study of a PPA portfolio of solar power plants in Germany illustrates how the variability of production less demand can be reduced significantly by a geographical hedge. The analysis in this chapter also has applications to energy systems planning.
Fred Espen Benth

Climate Risk

Frontmatter
Climate Risk in Structural Credit Models
Abstract
This survey article reviews the current state of literature on how structural models of credit risk are employed to model the impact of climate risk on financial markets. We discuss how the two prominent types of climate risk, physical and transition risk, are captured by the seminal Merton model and its well-known extensions. Theoretical and practical advantages and drawbacks are worked out and an outlook on possible model improvements is provided.
Alexander Blasberg, Rüdiger Kiesel
Metadata
Title
Quantitative Energy Finance
Editors
Fred Espen Benth
Almut E. D. Veraart
Copyright Year
2024
Electronic ISBN
978-3-031-50597-3
Print ISBN
978-3-031-50596-6
DOI
https://doi.org/10.1007/978-3-031-50597-3