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 2653

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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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Risks is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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 2 | Viewed by 2199
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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