Bayesian estimation of an extended local scale stochastic volatility model

Abstract

A new version of the local scale model of Shephard (1994) is presented. Its features are identically distributed evolution equation disturbances, the incorporation of in-the-mean effects, and the incorporation of variance regressors. A Bayesian posterior simulator and an exact simulation smoother are presented. The model is applied to simulated data and to publicly available exchange rate and asset return data. Simulation smoothing turns out to be essential for the accurate interval estimation of volatilities. Bayes factors show that the new model is competitive with GARCH and Lognormal stochastic volatility formulations. Its forecasting performance is comparable to GARCH.State space models; Markov chain Monte Carlo; simulation smoothing; generalized error distribution; generalized t distribution