024-23

第24回研究会

強化学習を用いた期待効用ベースヘッジ手法†

著者†

上田翼(三井住友DSアセットマネジメント)

概要†

Selling options is a popular investment strategy, which regularly receives a premium and, on the other hand, takes variance risk, especially negative fat-tail risk. Therefore, it is important for risk-averse investors to mitigate these types of risks by constructing hedge position in consideration of transaction costs. Main results of this research are as follows: (1) In a practical simulation, DDPG model with utility based reward suggests a better way of dynamic hedging compared to simple benchmarks. (2) As a real-world application to market data, this learned model successfully manages the short straddle portfolio of treasury futures options.

キーワード†

Reinforcement learning, Dynamic hedging, Expected utility

論文†

20_SIG-FIN-24.pdf