024-23

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[[第24回研究会>024]]

*強化学習を用いた期待効用ベースヘッジ手法 [#w3e64458]

**著者 [#ee82b693]
上田翼(三井住友DSアセットマネジメント)

**概要 [#n3b860ea]
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 [#tc3ea86e]
Reinforcement learning, Dynamic hedging, Expected utility

**論文 [#p35206d3]

(3月11日以降に公表いたします)
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//(3月11日以降に公表いたします)
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