Gradojevic Nikola, Kukolj Dragan
Department of Economics and Finance, University of Guelph, Lang School of Business and Economics, 50 Stone Road East, Guelph, ON N1G 2W1 Canada.
Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia.
Ann Oper Res. 2022 Feb 25:1-24. doi: 10.1007/s10479-022-04578-7.
This paper addresses the interpretability problem of non-parametric option pricing models by using the explainable artificial intelligence (XAI) approach. We study call options written on the S&P 500 stock market index across three market regimes: pre-COVID-19, COVID-19 market crash, and post-COVID-19 recovery. Our comparative option pricing exercise demonstrates the superiority of the random forest and extreme gradient boosting models for each market regime. We also show that the model's pricing accuracy has worsened from the pre-COVID-19 to the recovery period. Moneyness was the most important price determinants across the market regimes, while the implied volatility and time-to-maturity inputs contributed intermittently to a lesser extent. During the COVID-19 crash, open interest gained more economic importance due to the increased behavioral tendencies of traders consistent with market distress.
本文采用可解释人工智能（XAI）方法解决非参数期权定价模型的可解释性问题。我们研究了在标准普尔500股票市场指数上的看涨期权在三种市场状态下的情况：新冠疫情前、新冠疫情引发的市场崩溃以及新冠疫情后的复苏阶段。我们的比较期权定价实验证明了随机森林和极端梯度提升模型在每种市场状态下的优越性。我们还表明，从新冠疫情前到复苏阶段，模型的定价准确性有所下降。在所有市场状态下，实虚值状态是最重要的价格决定因素，而隐含波动率和到期时间输入在较小程度上间歇性地发挥作用。在新冠疫情引发的市场崩溃期间，由于交易员与市场困境相关的行为倾向增加，未平仓合约量具有了更大的经济重要性。
