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MCMC-based estimation of Markov Switching ARMA-GARCH models

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  • Jan Henneke
  • Svetlozar Rachev
  • Frank Fabozzi
  • Metodi Nikolov

Abstract

Regime switching models, especially Markov Switching (MS) models, are regarded as a promising way to capture nonlinearities in time series. Combining the elements of MS models with full Autoregressive Moving Average-Generalized Autoregressive Conditional Heteroskedasticity (ARMA-GARCH) models poses severe difficulties for the computation of parameter estimators. Existing methods can become completely unfeasible due to the full path dependence of such models. In this article, we demonstrate how to overcome this problem. We formulate a full MS-ARMA-GARCH model and its Bayes estimator. This facilitates the use of Markov Chain Monte Carlo methods and allows us to develop an algorithm to compute the Bayes estimator of the regimes and parameters of our model. The approach is illustrated on simulated data and with returns from the New York Stock Exchange (NYSE). Our model is then compared to other approaches and clearly proves to be advantageous.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Applied Economics.

Volume (Year): 43 (2011)
Issue (Month): 3 ()
Pages: 259-271

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Handle: RePEc:taf:applec:v:43:y:2011:i:3:p:259-271

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Cited by:
  1. BAUWENS, Luc & HAUTSCH, Nikolaus, . "Modelling financial high frequency data using point processes," CORE Discussion Papers RP -2123, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  2. Marcel Aloy & Gilles De Truchis & Gilles Dufrénot & Benjamin Keddad, 2013. "Shift-Volatility Transmission in East Asian Equity Markets," Working Papers halshs-00935364, HAL.
  3. BAUWENS, Luc & PREMINGER, Arie & ROMBOUTS, Jeroen V.K., 2007. "Theory and inference for a Markov switching GARCH model," CORE Discussion Papers 2007055, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  4. DUFAYS, Arnaud, 2012. "Infinite-state Markov-switching for dynamic volatility and correlation models," CORE Discussion Papers 2012043, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  5. Monica Billio & Roberto Casarin & Anthony Osuntuyi, 2012. "Efficient Gibbs Sampling for Markov Switching GARCH Models," Working Papers 2012:35, Department of Economics, University of Venice "Ca' Foscari".
  6. Szabolcs Blazsek & Anna Downarowicz, 2008. "Regime switching models of hedge fund returns," Faculty Working Papers 12/08, School of Economics and Business Administration, University of Navarra.
  7. Pierre-Julien Trombe & Pierre Pinson & Henrik Madsen, 2012. "A General Probabilistic Forecasting Framework for Offshore Wind Power Fluctuations," Energies, MDPI, Open Access Journal, vol. 5(3), pages 621-657, March.
  8. David Ardia & Lennart F. Hoogerheide, 2010. "Efficient Bayesian Estimation and Combination of GARCH-Type Models," Tinbergen Institute Discussion Papers 10-046/4, Tinbergen Institute.
  9. Monica Billio & Maddalena Cavicchioli, 2013. "“Markov Switching Models for Volatility: Filtering, Approximation and Duality”," Working Papers 2013:24, Department of Economics, University of Venice "Ca' Foscari".
  10. repec:luc:wpaper:14-07 is not listed on IDEAS

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