A Markov-Chain Sampling Algorithm for GARCH Models
AbstractThis paper describes a GAUSS program of a Markov-chain sampling algorithm for GARCH models proposed by Nakatsuma (1998). This algorithm allows us to generate Monte Carlo samples of parameters in a GARCH model from their joint posterior distribution. The samples obtained by this algorithm are used for Bayesian analysis of the GARCH model. As numerical examples, GARCH models of simulated data and of weekly foreign exchange rate series are estimated and analyzed.
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Bibliographic InfoArticle provided by De Gruyter in its journal Studies in Nonlinear Dynamics & Econometrics.
Volume (Year): 3 (1998)
Issue (Month): 2 (July)
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Web page: http://www.degruyter.com
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- David, Ardia, 2006. "Bayesian Estimation of the GARCH(1,1) Model with Normal Innovations," MPRA Paper 12985, University Library of Munich, Germany.
- Ardia, David & Hoogerheide, Lennart F., 2010.
"Efficient Bayesian estimation and combination of GARCH-type models,"
22919, University Library of Munich, Germany.
- David Ardia & Lennart F. Hoogerheide, 2010. "Efficient Bayesian Estimation and Combination of GARCH-Type Models," Tinbergen Institute Discussion Papers 10-046/4, Tinbergen Institute.
- J. Miguel Marín & M. T. Rodríguez Bernal & Eva Romero, 2013. "Data cloning estimation of GARCH and COGARCH models," Statistics and Econometrics Working Papers ws132723, Universidad Carlos III, Departamento de Estadística y Econometría.
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