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Bayesian Analysis of Stochastic Volatility Models (1994)
| Citations: | 601 - 26 self |
BibTeX
@MISC{Jacquier94bayesiananalysis,
author = {Eric Jacquier and Nicholas G. Polson and Peter E. Rossi},
title = {Bayesian Analysis of Stochastic Volatility Models},
year = {1994}
}
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Abstract
this article is to develop new methods for inference and prediction in a simple class of stochastic volatility models in which logarithm of conditional volatility follows an autoregressive (AR) times series model. Unlike the autoregressive conditional heteroscedasticity (ARCH) and gener- alized ARCH (GARCH) models [see Bollerslev, Chou, and Kroner (1992) for a survey of ARCH modeling], both the mean and log-volatility equations have separate error terms. The ease of evaluating the ARCH likelihood function and the ability of the ARCH specification to accommodate the timevarying volatility found in many economic time series has fostered an explosion in the use of ARCH models. On the other hand, the likelihood function for stochastic volatility models is difficult to evaluate, and hence these models have had limited empirical application
Keyphrases
stochastic volatility model bayesian analysis timevarying volatility series model conditional volatility new method arch model separate error term likelihood function empirical application log-volatility equation simple class many economic time series autoregressive conditional heteroscedasticity arch specification arch likelihood function
