What We Can Learn from Pricing 139,879 Individual Stock Options

Abstract

The GARCH framework has been used for option pricing with quite some success. While the initial work assumed conditional Gaussian innovations, recent contributions relax this assumption and allow for more flexible parametric specifications of the underlying distribution. However, until now the empirical applications have been limited to index options or options on only a few stocks and this using only few potential distributions and variance specifications. In this paper we test the GARCH framework on 30 stocks in the Dow Jones Industrial Average using two classical volatility specifications and 7 different underlying distributions. Our results provide clear support for using an asymmetric volatility specification together with non-Gaussian distribution, particularly of the Normal Inverse Gaussian type, and statistical tests show that this model is most frequently among the set of best performing models.

Keywords: American options, GARCH models, Model Confidence Set, Simulation

JEL Classification: C22, C53, G13