Statistical Inference for Random-Variance Option Pricing.

This article deals with the estimation of continuous-time stochastic volatility models of option pricing. We argue that option prices are much more informative about the parameters than are asset prices. This is confirmed in a Monte Carlo experiment that compares two very simple strategies based on the different information sets. Both approaches are based on indirect inference and avoid any discretization bias by simulating the continuous-time model. We assume an Ornstein-Uhlenbeck process for the log of the volatility, a zero-volatility risk premium, and no leverage effect. We do not pursue asymptotic efficiency or specification issues; rather, we stick to a framework with no overidentifying restrictions and show that, given our option-pricing model, estimation based on option prices is much more precise in samples of typical size, without increasing the computational burden.