J. Fan and L. Mancini: Option Pricing with Aggregation of Physical Models and Nonparametric Statistical Learning

December 9, 2006

Financial models are largely used in option pricing. These physical models capture several salient features of asset price dynamics. The pricing performance can be significantly enhanced when they are combined with nonparametric learning approaches, that empirically learn and correct pricing errors through estimating state price distributions. In this paper, we propose a new semi-parametric method for estimating state price distributions and pricing financial derivatives. This method is based on a physical model guided non parametric approach to estimate the state price distribution of a normalized state variable, called theAutomatic Correction of Errors (ACE) in pricing formulae.