Bayesian Analysis of Stochastic Volatility Models

Bayesian Analysis of Stochastic Volatility Models (1994)

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by Eric Jacquier , Nicholas G. Polson , Peter E. Rossi

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Datum	Value	Source
TITLE	Bayesian Analysis of Stochastic Volatility Models	SVM HeaderParse 0.1
AUTHOR NAME	Eric Jacquier	user correction - Legacy Corrections
AUTHOR AFFIL	Johnson Graduate School of Management, Cornell University, Ithaca	user correction - Legacy Corrections
AUTHOR ADDR	NY 14853	user correction - Legacy Corrections
AUTHOR NAME	Nicholas G. Polson	user correction - Legacy Corrections
AUTHOR AFFIL	Graduate School of Business, University of Chicago, Chicago	user correction - Legacy Corrections
AUTHOR ADDR	IL 60637	user correction - Legacy Corrections
AUTHOR NAME	Peter E. Rossi	user correction - Legacy Corrections
AUTHOR AFFIL	Graduate School of Business, University of Chicago, Chicago	user correction - Legacy Corrections
AUTHOR ADDR	IL 60637	user correction - Legacy Corrections
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	user correction - Legacy Corrections
YEAR	1994	user correction - Legacy Corrections
CITATIONS	35 found	ParsCit 1.0