A Rank-Based Reward between a Principal and a Field of Agents: Application to Energy Savings
We consider a problem where a Principal aims to design a reward function to a field of heterogeneous agents. In our setting, the agents compete with each other through their rank within the population in order to obtain the best reward. We first explicit the equilibrium for the mean-field game played by the agents, and then characterize the optimal reward in the homogeneous setting. For the general case of a heterogeneous population, we develop a numerical approach, which is then applied to the specific case study of the market of Energy Saving Certificates.
Clémence Alasseur is head of the quant team Energy and Financial Markets, Risk & Valuation at EDF R&D since 2021. Previously, she was Executive Director of the Finance for Energy Market Research Center. Before, she leaded projects on price risk management and electricity price modelling in collaboration with operational divisions such as the risk control. Her research interests are price models and tariffs in electricity system.
Fred Espen Benth
Recent advances on forward curve modeling and applications
In this talk we survey some recent advances on forward curve modeling. We present infinite-dimensional stochastic volatility models, including leverage, and discuss the question of option pricing in this context. Neural networks in Hilbert spaces provide an attractive numerical method to price options on forward curves, where stylised structures of the curves are used as additional information in the training. Finally, we present new limit theorems on the realised variation of forward curves, which can be used for estimation. The talk is based on joint works with Nils Detering (Santa Barbara), Luca Galimberti (Trondheim), Dennis Schroers (Bonn), Carlo Sgarra (Milano), Almut Veraart (London).
Fred Espen Benth is a professor of mathematics at the University of Oslo. His research is focused around energy finance, where he has co-authored two books: Stochastic Modelling of Electricity and Related Markets and Modelling and Pricing in Financial Markets for Weather Derivatives (both published on World Scientific). Recently he has worked on stochastic volatility models of forward and futures prices and machine learning techniques in pricing.
Electricity price forecasting: Data science meets fundamental models
Electricity price forecasting models have become better and better in recent years. Methodological advances and better data quality are the main reasons. Nevertheless, there are two structurally different model approaches in the literature: i) data science models, which evaluate historical price and external data, and ii) fundamental models, which model the electricity price economically using supply and demand approaches. First, we discuss advantages and disadvantages of both modeling approaches. Then, we consider several options on how to combine and intertwine the two modeling approaches to improve model performance and forecast accuracy.
It does not matter whether one is a proponent of i) using more linear models or deep neural networks; or a proponent of ii) using simple supply-stack models or sophisticated European energy market models. This talk demonstrates how data scientists should learn from fundamental modelers and vice versa.
Since 2017, Florian Ziel is Professor of Environmental Economics, esp. Economics of Renewable Energies, at the House of Energy Markets and Finance at the University of Duisburg-Essen, Germany. He received his MSc in statistics from University College Dublin (Ireland), his Diplom in mathematics from Dresden University of Technology (Germany) and his PhD on forecasting in energy markets from the European-University Viadrina in Frankfurt Oder (Germany). His research interests include modeling and forecasting energy markets and systems. He is the first author of various peer-reviewed journal articles, most notably in top-tier IEEE Transactions on Power Systems, Applied Energy, Energy Economics, Renewable and Sustainable Energy Reviews and International Journal of Forecasting.