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Hidden Markov models with t components. Increased persistence and other aspects

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  • Bulla, Jan

Abstract

Hidden Markov models have been applied in many different fields during the last decades, including econometrics and finance. However, the lion’s share of the investigated models is Markovian mixtures of Gaussian distributions. We present an extension to conditional t-distributions, including models with unequal distribution types in different states. It is shown that the extended models, on the one hand, reproduce various stylized facts of daily returns better than the common Gaussian model. On the other hand, robustness to outliers and persistence of the visited states increases significantly.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 21830.

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Date of creation: Oct 2009
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Handle: RePEc:pra:mprapa:21830

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Keywords: Hidden Markov model; Markov-switching model; state persistence; t-distribution; daily returns;

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  1. Stephen G. Cecchetti & Pok-sang Lam & Nelson C. Mark, 1988. "Mean Reversion in Equilibrium Asset Prices," NBER Working Papers 2762, National Bureau of Economic Research, Inc.
  2. C. W. J. GRANGER & Zhuanxin DING, 1995. "Some Properties of Absolute Return: An Alternative Measure of Risk," Annales d'Economie et de Statistique, ENSAE, issue 40, pages 67-91.
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  5. Bulla, Jan & Bulla, Ingo, 2006. "Stylized facts of financial time series and hidden semi-Markov models," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 51(4), pages 2192-2209, December.
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  9. Rydén, Tobias & Teräsvirta, Timo & Åsbrink, Stefan, 1996. "Stylized Facts of Daily Return Series and the Hidden Markov Model," Working Paper Series in Economics and Finance, Stockholm School of Economics 117, Stockholm School of Economics.
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  14. Robert Breunig & Serinah Najarian & Adrian Pagan, 2003. "Specification Testing of Markov Switching Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, Department of Economics, University of Oxford, vol. 65(s1), pages 703-725, December.
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Cited by:
  1. M. Bernardi & L. Petrella, 2014. "Interconnected risk contributions: an heavy-tail approach to analyse US financial sectors," Papers 1401.6408, arXiv.org, revised Apr 2014.
  2. Trojan, Sebastian, 2013. "Regime Switching Stochastic Volatility with Skew, Fat Tails and Leverage using Returns and Realized Volatility Contemporaneously," Economics Working Paper Series, University of St. Gallen, School of Economics and Political Science 1341, University of St. Gallen, School of Economics and Political Science, revised Aug 2014.

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