A comparison of Bayesian model selection based on MCMC with an application to GARCH-type models

This paper presents a comprehensive review and comparison of five computational methods for Bayesian model selection, based on MCMC simulations from posterior model parameter distributions. We apply these methods to a well-known and important class of models in financial time series analysis, namely GARCH and GARCH-t models for conditional return distributions (assuming normal and t-distributions).(...)

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Year of publication:	2003-11-01
Authors:	Miazhynskaia, Tatiana ; Frühwirth-Schnatter, Sylvia ; Dorffner, Georg
Publisher:	Wirtschaftsuniversität <Wien>
Subject:	Markov-Ketten-Monte-Carlo-Verfahren \| Markov-Chain Monte Carlo \| Bayes-Inferenz \| GARCH-Prozess

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Extent:	332800 bytes 27 p. application/pdf
Series:	Wirtschaftsuniversität Wien - Working Papers Arbeitspapier ; 2003, 83
Type of publication:	Book / Working Paper
Language:	English
Classification:	Financial theory ; Statistical Methods ; Individual Working Papers, Preprints ; No country specification
Source:	USB Cologne (business full texts)

Persistent link: https://www.econbiz.de/10005844729