Spread Option Pricing Method Based on Nonparametric Predictive Inference Copula

This paper introduces a novel spread option pricing model, the nonparametric predictive inference–based copula spread option model (NPIC-SOM), designed to evaluate the interdependence of multiple underlying assets. Through empirical analysis focused on Brent-WTI spread options, a widely traded derivative, we compare the predictive performance of the NPIC-SOM against the traditional geometric Brownian motion crack spread option model (GBM-CSOM). Our findings reveal that the NPIC-SOM not only forecasts spread option prices closer to empirical values but also captures market fluctuations more accurately than the GBM-CSOM. This superiority extends across various option types, moneyness levels and delta hedge efficiency. Furthermore, the NPIC-SOM's reliance on time-varying parameters enhances prediction accuracy, particularly for extreme market scenarios. These results indicate the practicality and efficiency of the NPIC-SOM as a robust spread option pricing model, offering valuable insights for option pricing strategies in financial markets.