Work

Dynamic hedging of 50ETF options using Proximal Policy Optimization

Year: 2025

Type: article

Abstract: This paper employs the PPO (Proximal Policy Optimization) algorithm to study the risk hedging problem of the Shanghai Stock Exchange (SSE) 50ETF options. First, the action and state spaces were designed based on the characteristics of the hedging task, and a reward function was developed according to the cost function of the options. Second, combining the concept of curriculum learning, the agent was guided to adopt a simulated-to-real learning approach for dynamic hedging tasks, reducing the learning difficulty and addressing the issue of insufficient option data. A dynamic hedging strategy for 50ETF options was constructed. Finally, numerical experiments demonstrate the superiority of the designed algorithm over traditional hedging strategies in terms of hedging effectiveness.

Source: Journal of Automation and Intelligence

DOI: 10.1016/j.jai.2025.04.001

Authors: Lei Liu, Mengmeng Hao, Jinde Cao

Institutions: Hohai University, Southeast University

Language: English

Topic: Stochastic processes and financial applications

Subfield: Finance

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: diamond

Funders: National Natural Science Foundation of China, Aeronautical Science Foundation of China, Anhui Sanlian University, Key Laboratory of System Control and Information Processing

Awards: G4745889518, G4982041260, G5658853132