Naslov (eng)

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

Autor

Gradojević, Nikola
Kukolj, Dragan

Opis (eng)

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.

Jezik

engleski

Datum

2022

Licenca

Ovo delo je licencirano pod uslovima licence
Creative Commons CC BY 4.0 - Creative Commons Autorstvo 4.0 International License.

http://creativecommons.org/licenses/by/4.0/legalcode

Predmet

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

Deo kolekcije (1)

o:1600

Radovi profesora i saradnika Fakulteta za ekonomiju i inženjerski menadžment