Deep Generators on Commodity Markets; application to Deep Hedging

TL;DR

This paper evaluates four advanced deep generative models on commodity market time series and demonstrates their application in data-driven deep hedging of commodity options, highlighting their potential in financial risk management.

Contribution

It adapts and tests four state-of-the-art deep generative models specifically for commodity market time series and applies them to deep hedging, a novel approach in this context.

Findings

Generative models effectively produce realistic commodity time series.

Deep hedging trained on generated data performs well in risk mitigation.

The study showcases the feasibility of data-driven risk hedging using generative models.

Abstract

Driven by the good results obtained in computer vision, deep generative methods for time series have been the subject of particular attention in recent years, particularly from the financial industry. In this article, we focus on commodity markets and test four state-of-the-art generative methods, namely Time Series Generative Adversarial Network (GAN) Yoon et al. [2019], Causal Optimal Transport GAN Xu et al. [2020], Signature GAN Ni et al. [2020] and the conditional Euler generator Remlinger et al. [2021], are adapted and tested on commodity time series. A first series of experiments deals with the joint generation of historical time series on commodities. A second set deals with deep hedging of commodity options trained on he generated time series. This use case illustrates a purely data-driven approach to risk hedging.