Continuous Time Garch-Based Modeling and Filtering
Author | : Fabio Fornari |
Publisher | : |
Total Pages | : 0 |
Release | : 2002 |
ISBN-10 | : OCLC:1375590778 |
ISBN-13 | : |
Rating | : 4/5 (78 Downloads) |
Book excerpt: Aim of this article is to judge the empirical performance of Arch as diffusion approximations to models of the short-term rate with stochastic volatility and as filters of the unobserved volatility. We show that the estimation of the continuous time scheme to which a discrete time Arch model converges can be safely based on simple moment conditions linking the discrete time to the continuous time coefficients. A natural substitute of a global specification test for just-identified problems based on indirect inference shows in fact that this approximation to diffusions gives rise to a negligible disaggregation bias. Unlike previous literature in which standard Arch models approximated only specific diffusions, our estimation strategy relies on a new Arch that approximates any Cev-diffusion model for the conditional volatility. A Monte-Carlo study reveals that the filtering performances of this model is remarkably good, even in the presence of an important kind of misspecification.