JSM 2005 - Toronto

Abstract #304032

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 93
Type: Topic Contributed
Date/Time: Monday, August 8, 2005 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract - #304032
Title: Bayesian Inference on Mixture-of-Experts for Estimation of Stochastic Volatility
Author(s): Gabriel Huerta*+
Companies: The University of New Mexico
Address: Department of Mathematics and Statistics, Albuquerque, NM, 87131-0001, United States
Keywords: Mixture ; Stochastic Volatility ; covariates ; ARCH/GARCH/EGARCH ; MCMC
Abstract:

The problem of model mixing in time series, for which the interest lies in the estimation of stochastic volatility, is addressed using the approach known as Mixture-of-Experts (ME). Specifically, this work proposes a ME model where the experts are defined through ARCH, GARCH, and EGARCH structures. Estimates of the predictive distribution of volatilities are obtained using a full Bayesian approach. The methodology is illustrated with an analysis of a section of U.S. dollar/German Mark exchange rates and a study of the Mexican stock market (IPC) index using the Dow Jones Industrial (DJI) index as a covariate.

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