Statistics and Risk Management in the Energy Markets

A special issue of Risks (ISSN 2227-9091).

Deadline for manuscript submissions: closed (10 December 2022) | Viewed by 3386

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Guest Editor
Electricité de France R&D, 91120 Palaiseau, France
Interests: statistical modelling; risk management and pricing on energy markets; statistical estimation forecasting
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Special Issue Information

Dear Colleagues,

The energy markets are at the heart of the news and public debates following the COVID-19 crisis, several climatic disasters such as the crisis in Texas, and more recently the Ukrainian crisis. Fuel and electricity prices have exploded, both on the spot and forward markets. In this Special Issue, we are particularly interested in work that allows us to better understand the future of these markets. Thus, work allowing statistical analyses of these markets, but also innovative modeling, in particular with a link between market prices and exogenous variables such as climate variables, is welcome. We are also interested in work on risk management in this environment where climate risks have an impact on market prices. The Special Issue may also include work on short-term markets such as intraday markets, which are developing considerably, especially due to the high uncertainties caused by the increasing production of renewable energy.

Dr. Olivier Féron
Guest Editor

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Published Papers (1 paper)

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Research

18 pages, 2287 KiB  
Article
Deep Generators on Commodity Markets Application to Deep Hedging
by Nicolas Boursin, Carl Remlinger and Joseph Mikael
Risks 2023, 11(1), 7; https://doi.org/10.3390/risks11010007 - 23 Dec 2022
Cited by 4 | Viewed by 2870
Abstract
Four deep generative methods for time series are studied on commodity markets and compared with classical probabilistic models. The lack of data in the case of deep hedgers is a common flaw, which deep generative methods seek to address. In the specific case [...] Read more.
Four deep generative methods for time series are studied on commodity markets and compared with classical probabilistic models. The lack of data in the case of deep hedgers is a common flaw, which deep generative methods seek to address. In the specific case of commodities, it turns out that these generators can also be used to refine the price models by tackling the high-dimensional challenges. In this work, the synthetic time series of commodity prices produced by such generators are studied and then used to train deep hedgers on various options. A fully data-driven approach to commodity risk management is thus proposed, from synthetic price generation to learning risk hedging policies. Full article
(This article belongs to the Special Issue Statistics and Risk Management in the Energy Markets)
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