DEEP GENERATORS ON COMMODITY MARKETS APPLICATION TO DEEP HEDGING

Abstract

Four deep generative methods for time series are studied on commodity mar- kets 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 also allow to refine the price signals model tackle the high dimension flaw. 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 for commodity risk management is proposed, from generating synthetic prices to learning risk hedging policies.