Gamma and Vega Hedging Using Deep Distributional Reinforcement Learning

2022/05/10 by Jay Cao, Cao, Jay, Jacky Chen +11 · 1 citation
Decision Sciences · Economics, Econometrics and Finance · #Computational Engineering #Computational Finance (q-fin.CP) #FOS: Computer and information sciences #FOS: Economics and business #Finance #Financial Markets and Investment Strategies #Market Dynamics and Volatility #Stock Market Forecasting Methods #and Science (cs.CE)

paper · pdf · doi:10.48550/arxiv.2205.05614

openalex publication_date 2022/05/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We show how D4PG can be used in conjunction with quantile regression to develop a hedging strategy for a trader responsible for derivatives that arrive stochastically and depend on a single underlying asset. We assume that the trader makes the portfolio delta neutral at the end of each day by taking a position in the underlying asset. We focus on how trades in the options can be used to manage gamma and vega. The option trades are subject to transaction costs. We consider three different objective functions. We reach conclusions on how the optimal hedging strategy depends on the trader's objective function, the level of transaction costs, and the maturity of the options used for hedging. We also investigate the robustness of the hedging strategy to the process assumed for the underlying asset.

Cited by

• Distributional Reinforcement Learning on Path-dependent Options

Related

• Deep Hedging
• Option Dynamic Hedging Using Reinforcement Learning
• Hedging with Sparse Reward Reinforcement Learning
• EX-DRL: Hedging Against Heavy Losses with EXtreme Distributional Reinforcement Learning
• Deep Hedging with Reinforcement Learning: A Practical Framework for Option Risk Management