Deep Generators on Commodity Markets Application to Deep Hedging

Nicolas Boursin (), Carl Remlinger and Joseph Mikael ()
Additional contact information
Nicolas Boursin: EDF Lab Singapore, 1 Lor 2 Toa Payoh, #04-02 Braddell House, Singapore 319637, Singapore
Carl Remlinger: LAMA, Université Gustave Eiffel, 16 Bd Newton, 77420 Champs-sur-Marne, France
Joseph Mikael: FiME Laboratory, EDF Lab, Bd Gaspard Monge, 91120 Palaiseau, France

Risks, 2022, vol. 11, issue 1, 1-18

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 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.

Keywords: time series; generative methods; GAN; deep hedging; energy markets (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/2227-9091/11/1/7/pdf (application/pdf)
https://www.mdpi.com/2227-9091/11/1/7/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jrisks:v:11:y:2022:i:1:p:7-:d:1013290

Access Statistics for this article

Risks is currently edited by Mr. Claude Zhang

More articles in Risks from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().