o:6519 Unlocking the black box: Non-parametric option pricing before and during COVID-19 en Abstract: 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 deter minants across the market regimes, while the implied volatility and time-to-maturity inputs contributedintermittently toalesserextent.DuringtheCOVID-19crash,openinterestgained more economic importance due to the increased behavioral tendencies of traders consistent with market distress. Keywords: Option pricing, COVID-19, Random forest, Extreme gradient boosting, Explainable artificial intelligence, Interpretability 1552099 10.1007/s10479-022-04578-7 2024-12-12T10:30:56.221Z 44 yes 46 Nikola Gradojević Fakultet za ekonomiju i inženjerski menadžment u Novom Sadu 0000-0003-4001-3159 Dragan Kukolj person 2022 application/pdf 817716 https://unilib.phaidrabg.rs/o:6519 no yes 16 70 1552253 2022 Annals of Operations Research 334 59 82 2022