Markov Chain Monte Carlo Methods for Generalized Stochastic Volatility Models
Published 1 October 2000
Siddhartha Chib, Neil Shephard, Federico Nardari
Citations28
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Abstract
This paper is co#FW]G4# with Marko v chainMo# te Carlo based Bayesian inference in generalizedmo delso fsto chastic vo latility defined by heavy-tailed student-t distributio # s (with unkno wn degreeso# freedo# ) andco variate e#ects in the o# servatio n and vo latility equatio ns. A simple, fast and highly e#cient algo rithm, that buildso n Kim, Shephard and Chib (1998), is develo# ed fo r estimating these mo dels. Co# putatio no f the likeliho o d functio# by a particle filter isco#:H:]#54 as are metho ds fo r co#6G6R#54:6 diagno stic measures and the mo del marginal likeliho o d. The techniques are applied in detailto daily returnso n the S&P 500 index andto weekly changes in thesho rt-term interest rate. Keywords: Bayes facto# , Marko v chain mo nte car lo# marginal likeliho o d, mixture mo dels, particle filters, simulatio# based inference,sto chastic vo latility. 1 Introduction The e#cient fitting o# mo dels withsto chastic vo latility iso ne o# the m o# e challenging pro blems...
Keywords
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