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


  • They are grateful for valuable comments from conference participants at the Australasian Meeting of the Econometric Society and an anonymous referee.

* Address for correspondence: Soosung Hwang, Faculty of Finance, Cass Business School, 106 Bunhill Row, London EC1Y 8TZ, UK.


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.