Deep SmoothingとDeep Hedgingを用いたBTC市場における複数オプションのヘッジ戦略分析

抄録

Deep Hedging has garnered attention as a novel approach utilizing deep learning to address challenges in pricing and hedging in incomplete financial markets．However, the effectiveness of Deep Hedging when applied to multiple options has not been thoroughly examined．Additionally, financial market data is often noisy, making it challenging to train deep learning models effectively. Therefore, in this study, we aim to address these issues by evaluating the effectiveness of Deep Hedging for multiple options using data from the Bitcoin options market．We verify its effectiveness for multiple options and assess the impact of introducing smoothing techniques. Specifically, we introduce a technique called Deep Smoothing to reduce noise and prevent arbitrage opportunities when dealing with a portfolio composed of multiple European options with the same underlying asset, the same maturity, but different strike prices．We combine this smoothing technique with the structure of the Implied Volatility Smile（IVS）to propose a new framework of Deep Hedging for multiple options. We validate our empirical results with Bitcoin options market data, demonstrating that: （1） Deep Hedging outperforms traditional delta hedging, （2） when hedging multiple options, our method achieves performance equal to or better than conventional Deep Hedging targeting a single option, and （3） the application of Deep Smoothing to the input data leads to improved hedging performance．