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How Persistent is Stock Return Volatility? An Answer with Markov Regime Switching Stochastic Volatility Models

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  • Soosung Hwang
  • Steve E. Satchell
  • Pedro L. Valls Pereira

Abstract

We propose generalised stochastic volatility models with Markov regime changing state equations (SVMRS) to investigate the important properties of volatility in stock returns, specifically high persistence and smoothness. The model suggests that volatility is far less persistent and smooth than the conventional GARCH or stochastic volatility. Persistent short regimes are more likely to occur when volatility is low, while far less persistence is likely to be observed in high volatility regimes. Comparison with different classes of volatility supports the SVMRS as an appropriate proxy volatility measure. Our results indicate that volatility could be far more difficult to estimate and forecast than is generally believed. Copyright 2007 The Authors Journal compilation (c) 2007 Blackwell Publishing Ltd.

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Bibliographic Info

Article provided by Wiley Blackwell in its journal Journal of Business Finance & Accounting.

Volume (Year): 34 (2007-06)
Issue (Month): 5-6 ()
Pages: 1002-1024

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Handle: RePEc:bla:jbfnac:v:34:y:2007-06:i:5-6:p:1002-1024

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Cited by:
  1. Massimo Guidolin, 2011. "Markov Switching Models in Empirical Finance," Working Papers 415, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  2. Anne Peguin-Feissolle & Gilles Dufrénot & Dominique Guegan, 2006. "Changing-regime volatility : A fractionally integrated SETAR model," Working Papers halshs-00410540, HAL.
  3. repec:hal:cesptp:halshs-00390636 is not listed on IDEAS
  4. Subbotin, Alexandre, 2009. "Volatility Models: from Conditional Heteroscedasticity to Cascades at Multiple Horizons," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 15(3), pages 94-138.
  5. Pan, Qi & Li, Yong, 2013. "Testing volatility persistence on Markov switching stochastic volatility models," Economic Modelling, Elsevier, vol. 35(C), pages 45-50.

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