Estimating and forecasting structural breaks in financial time series
AbstractWe present an algorithm, based on a differential evolution MCMC method, for Bayesian inference in AR-GARCH models subject to an unknown number of structural breaks at unknown dates. Break dates are directly treated as parameters and the number of breaks is determined by the marginal likelihood criterion. We prove the convergence of the algorithm and we show how to compute marginal likelihoods. We allow for both pure change-point and recurrent regime specifications and we show how to forecast structural breaks. We illustrate the efficiency of the algorithm through simulations and we apply it to eight financial time series of daily returns over the period 1987-2011. We find at least three breaks in all series.
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Bibliographic InfoPaper provided by Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) in its series CORE Discussion Papers with number 2011055.
Date of creation: 21 Nov 2011
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Bayesian inference; structural breaks; differential evolution; change-point; recurrent states; break forecasting; marginal likelihood;
Find related papers by JEL classification:
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Belleflamme,Paul & Peitz,Martin, 2010. "Industrial Organization," Cambridge Books, Cambridge University Press, number 9780521681599, October.
- Christina Amado & Timo Teräsvirta, 2008.
"Modelling Conditional and Unconditional Heteroskedasticity with Smoothly Time-Varying Structure,"
CREATES Research Papers
2008-08, School of Economics and Management, University of Aarhus.
- Cristina Amado & Timo Teräsvirta, 2008. "Modelling Conditional and Unconditional Heteroskedasticity with Smoothly Time-Varying Structure," NIPE Working Papers 03/2008, NIPE - Universidade do Minho.
- Amado, Cristina & Teräsvirta, Timo, 2008. "Modelling Conditional and Unconditional Heteroskedasticity with Smoothly Time-Varying Structure," Working Paper Series in Economics and Finance 691, Stockholm School of Economics.
- M. Hashem Pesaran & Davide Pettenuzzo & Allan Timmermann, 2004.
"Forecasting Time Series Subject to Multiple Structural Breaks,"
CESifo Working Paper Series
1237, CESifo Group Munich.
- M. Hashem Pesaran & Davide Pettenuzzo & Allan Timmermann, 2006. "Forecasting Time Series Subject to Multiple Structural Breaks," Review of Economic Studies, Oxford University Press, vol. 73(4), pages 1057-1084.
- Pesaran, M.H. & Pettenuzzo, D. & Timmermann, A., 2004. "‘Forecasting Time Series Subject to Multiple Structural Breaks’," Cambridge Working Papers in Economics 0433, Faculty of Economics, University of Cambridge.
- Pesaran, M Hashem & Pettenuzzo, Davide & Timmermann, Allan G, 2004. "Forecasting Time Series Subject to Multiple Structural Breaks," CEPR Discussion Papers 4636, C.E.P.R. Discussion Papers.
- Pesaran, M. Hashem & Pettenuzzo, Davide & Timmermann, Allan, 2004. "Forecasting Time Series Subject to Multiple Structural Breaks," IZA Discussion Papers 1196, Institute for the Study of Labor (IZA).
- Bollerslev, Tim, 1986.
"Generalized autoregressive conditional heteroskedasticity,"
Journal of Econometrics,
Elsevier, vol. 31(3), pages 307-327, April.
- Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
- Chib, Siddhartha, 1996. "Calculating posterior distributions and modal estimates in Markov mixture models," Journal of Econometrics, Elsevier, vol. 75(1), pages 79-97, November.
- repec:bla:restud:v:73:y:2006:i:4:p:1057-1084 is not listed on IDEAS
- Robert F. Engle & Jose Gonzalo Rangel, 2008. "The Spline-GARCH Model for Low-Frequency Volatility and Its Global Macroeconomic Causes," Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1187-1222, May.
- Fleurbaey,Marc & Maniquet,François, 2011.
"A Theory of Fairness and Social Welfare,"
Cambridge University Press, number 9780521715348, October.
- Ardia, David & Hoogerheide, Lennart F. & van Dijk, Herman K., 2008. "AdMit: Adaptive Mixtures of Student-t Distributions," DQE Working Papers 10, Department of Quantitative Economics, University of Freiburg/Fribourg Switzerland, revised 07 Jan 2009.
- Haas, Markus & Mittnik, Stefan & Paolella, Marc S., 2002.
"Mixed normal conditional heteroskedasticity,"
CFS Working Paper Series
2002/10, Center for Financial Studies (CFS).
- Duranton, Gilles & Martin, Philippe & Mayer, Thierry & Mayneris, Florian, 2010. "The Economics of Clusters: Lessons from the French Experience," OUP Catalogue, Oxford University Press, number 9780199592203.
- Chib S. & Jeliazkov I., 2001. "Marginal Likelihood From the Metropolis-Hastings Output," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 270-281, March.
- Hillebrand, Eric, 2005. "Neglecting parameter changes in GARCH models," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 121-138.
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