Unlocking the black box: Non-parametric option pricing before and during COVID-19

Nikola Gradojevic; Dragan Kukolj

doi:10.1007/s10479-022-04578-7

Unlocking the black box: Non-parametric option pricing before and during COVID-19

Ann Oper Res. 2022 Feb 25:1-24. doi: 10.1007/s10479-022-04578-7. Online ahead of print.

Authors

Nikola Gradojevic^{1

2}, Dragan Kukolj²

Affiliations

¹ Department of Economics and Finance, University of Guelph, Lang School of Business and Economics, 50 Stone Road East, Guelph, ON N1G 2W1 Canada.
² Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia.

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 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.

Keywords: COVID-19; Explainable artificial intelligence; Extreme gradient boosting; Interpretability; Option pricing; Random forest.