论文标题
使用神经SDE市场模型的对冲选项书籍
Hedging option books using neural-SDE market models
论文作者
论文摘要
我们研究了无套利神经SDE市场模型的能力，可以为对冲选择提供有效的策略。特别是，我们使用这些模型得出了基于灵敏度和最小值的对冲策略，并使用现实世界数据将其应用于各种选项投资组合时检查其性能。通过对典型和压力市场时期的进行回测分析，我们表明，与黑色 - choles Delta和Delta-Vega相比，神经SDE市场模型随着时间的流逝而达到的对冲错误较低，并且对对冲工具的男高音选择不太敏感。此外，使用市场模型的对冲与使用Heston模型的对冲相似，而前者在压力市场期间往往更健壮。
We study the capability of arbitrage-free neural-SDE market models to yield effective strategies for hedging options. In particular, we derive sensitivity-based and minimum-variance-based hedging strategies using these models and examine their performance when applied to various option portfolios using real-world data. Through backtesting analysis over typical and stressed market periods, we show that neural-SDE market models achieve lower hedging errors than Black--Scholes delta and delta-vega hedging consistently over time, and are less sensitive to the tenor choice of hedging instruments. In addition, hedging using market models leads to similar performance to hedging using Heston models, while the former tends to be more robust during stressed market periods.
