A Bayesian Method of Change-Point Estimation with Recurrent Regimes: Application to GARCH Models

41 Pages Posted: 9 May 2017

See all articles by Luc Bauwens

Luc Bauwens

Université catholique de Louvain

Bruno De Backer

National Bank of Belgium

Arnaud Dufays

Université catholique de Louvain, CORE

Date Written: April 29, 2014

Abstract

We present an estimation and forecasting method, based on a differential evolution MCMC method, for inference in GARCH models subjected to an unknown number of structural breaks at unknown dates. We treat break dates as parameters and determine the number of breaks by computing the marginal likelihoods of competing models. We allow for both recurrent and non-recurrent (change-point) regime specifications. We illustrate the estimation method through simulations and apply it to seven financial time series of daily returns. We find structural breaks in the volatility dynamics of all series and recurrent regimes in nearly all series. Finally, we carry out a forecasting exercise to evaluate the usefulness of structural break models.

Keywords: Bayesian Inference, MCMC, Structural Breaks, Recurrent Regimes, Marginal Likelihood, GARCH, Forecasting

JEL Classification: C11, C15, C22, C53, C58

Suggested Citation

Bauwens, Luc and De Backer, Bruno and Dufays, Arnaud, A Bayesian Method of Change-Point Estimation with Recurrent Regimes: Application to GARCH Models (April 29, 2014). Available at SSRN: https://ssrn.com/abstract=2965494 or http://dx.doi.org/10.2139/ssrn.2965494

Luc Bauwens (Contact Author)

Université catholique de Louvain ( email )

CORE
34 Voie du Roman Pays
B-1348 Louvain-la-Neuve, b-1348
Belgium
32 10 474321 (Phone)
32 10 474301 (Fax)

Bruno De Backer

National Bank of Belgium ( email )

Brussels, B-1000
Belgium

Arnaud Dufays

Université catholique de Louvain, CORE ( email )

Place Montesquieu, 3
Louvain-la-Neuve, 1348
Belgium

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