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

Abstract
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.
Authors
Nikola Gradojevic and Dragan Kukolj Nikola Gradojevic: University of Guelph Lang School of Business and Economics Dragan Kukolj: University of Novi Sad
Keywords
Option pricing ; COVID-19 ; Random forest ; Extreme gradient boosting ; Explainable artificial intelligence ; Interpretability (search for similar items in EconPapers)
Rank
0.74
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Gradient Boosting
Series
Annals of Operations Research 2024 vol. 334 issue 1 No 3 59-82
Time Added
2024/03/18 03:34
Total Downloads
0
Year Published
2024
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