Bayesian Inference on Mixture-of-Experts for Estimation of Stochastic Volatility

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 US dollar/German mark exchange rates and a study of the Mexican stock market index using the Dow Jones Industrial index as a covariate.

Citation

Villagran, A. and Huerta, G. (2006), "Bayesian Inference on Mixture-of-Experts for Estimation of Stochastic Volatility", Fomby, T.B. and Terrell, D. (Ed.) Econometric Analysis of Financial and Economic Time Series (Advances in Econometrics, Vol. 20 Part 2), Emerald Group Publishing Limited, Leeds, pp. 277-296. https://doi.org/10.1016/S0731-9053(05)20030-0