On the relative performance of some parametric and nonparametric estimators of option prices

TL;DR

This paper compares parametric models like Heston and variance-gamma with nonparametric Hermite function expansions for option pricing, showing Hermite models can outperform traditional models in empirical accuracy.

Contribution

It provides a detailed empirical comparison of parametric and nonparametric estimators for option prices, highlighting the effectiveness of Hermite expansions in practical settings.

Findings

Hermite expansions achieve competitive accuracy with few terms.

Hermite estimators can outperform Heston model in pricing errors.

Hermite approximations are less effective for variance-gamma densities.

Abstract

We examine the empirical performance of some parametric and nonparametric estimators of prices of options with a fixed time to maturity, focusing on variance-gamma and Heston models on one side, and on expansions in Hermite functions on the other side. The latter class of estimators can be seen as perturbations of the classical Black-Scholes model. The comparison between parametric and Hermite-based models having the same "degrees of freedom" is emphasized. The main criterion is the out-of-sample relative pricing error on a dataset of historical option prices on the S&P500 index. Prior to the main empirical study, the approximation of variance-gamma and Heston densities by series of Hermite functions is studied, providing explicit expressions for the coefficients of the expansion in the former case, and integral expressions involving the explicit characteristic function in the latter case. Moreover, these approximations are investigated numerically on a few test cases, indicating that expansions in Hermite functions with few terms achieve competitive accuracy in the estimation of Heston densities and the pricing of (European) options, but they perform less effectively with variance-gamma densities. On the other hand, the main large-scale empirical study show that parsimonious Hermite estimators can even outperform the Heston model in terms of pricing errors. These results underscore the trade-offs inherent in model selection and calibration, and their empirical fit in practical applications.