Conferences at Department of Economics, University of Toronto, RCEF 2012: Cities, Open Economies, and Public Policy

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Marginal Likelihood for Markov-Switching and Change-Point Garch Models

Luc Bauwens, Arnaud Dufays, Jeroen Rombouts

Last modified: 2012-06-27

Abstract


GARCH volatility models with fixed parameters are too restrictive for long time series due to breaks in theĀ  volatility process. Flexible alternatives are Markov-switching GARCH and
change-point GARCH models. They require estimation by MCMC methods due to the path dependence problem. An unsolved issue is the computation of their marginal likelihood, which is
essential for determiningĀ  the number of regimes or change-points. We solve the problem by using particle MCMC, a technique proposed by Andrieu, Doucet and Holenstein (2010). We examine the performance of this new method on simulated data, and we illustrate its use on several return series.

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