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- 024-23 へ行く。
[[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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