2025/04/07 by Andrei Neagu, Frédéric Godin, Neagu, Andrei +3 · 1 voice · 1 citation
Business, Management and Accounting · Computer Science · Decision Sciences · Economics, Econometrics and Finance · #Artificial Intelligence (cs.AI) #Computational Engineering #Computational Finance (q-fin.CP) #FOS: Computer and information sciences #FOS: Economics and business #Finance #Risk Management in Financial Firms #Risk and Portfolio Optimization #Stochastic processes and financial applications #and Science (cs.CE) #cs.AI #cs.CE #q-fin.CP
paper · pdf · doi:10.48550/arxiv.2504.05521
openalex publication_date 2025/04/07 · arxiv published 2025/04/07 · arxiv updated 2025/04/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Dynamic hedging is a financial strategy that consists in periodically transacting one or multiple financial assets to offset the risk associated with a correlated liability. Deep Reinforcement Learning (DRL) algorithms have been used to find optimal solutions to dynamic hedging problems by framing them as sequential decision-making problems. However, most previous work assesses the performance of only one or two DRL algorithms, making an objective comparison across algorithms difficult. In this paper, we compare the performance of eight DRL algorithms in the context of dynamic hedging; Monte Carlo Policy Gradient (MCPG), Proximal Policy Optimization (PPO), along with four variants of Deep Q-Learning (DQL) and two variants of Deep Deterministic Policy Gradient (DDPG). Two of these variants represent a novel application to the task of dynamic hedging. In our experiments, we use the Black-Scholes delta hedge as a baseline and simulate the dataset using a GJR-GARCH(1,1) model. Results show that MCPG, followed by PPO, obtain the best performance in terms of the root semi-quadratic penalty. Moreover, MCPG is the only algorithm to outperform the Black-Scholes delta hedge baseline with the allotted computational budget, possibly due to the sparsity of rewards in our environment.