Parametric models and non-parametric machine learning models for predicting option prices: Empirical comparison study over KOSPI 200 Index options

Abstract

We investigated the performance of parametric and non-parametric methods concerning the in-sample pricing and out-of-sample prediction performances of index options. Comparisons were performed on the KOSPI 200 Index options from January 2001 to December 2010. To verify the statistical differences between the compared methods, we tested the following null hypothesis: two series of forecasting errors have the same mean-squared value. The experimental study reveals that non-parametric methods significantly outperform parametric methods on both in-sample pricing and out-of-sample pricing. The outperforming non-parametric method is statistically different from the other models, and significantly different from the parametric models. The Gaussian process model delivers the most outstanding performance in forecasting, and also provides the predictive distribution of option prices.