Volatility Dynamics Under Duration-Dependent Mixing

28 Pages Posted: 18 Apr 2002 Last revised: 2 Mar 2012

See all articles by John M. Maheu

John M. Maheu

McMaster University - Michael G. DeGroote School of Business; RCEA

Thomas H. McCurdy

University of Toronto - Rotman School of Management


This paper proposes a discrete-state stochastic volatility model with duration-dependent mixing. The latter is directed by a high-order Markov chain with a sparse transition matrix. As in the standard first-order Markov-switching (MS)model, this structure can capture turning points and shifts in volatility due, for example, to policy changes or news events. However, the duration-dependent Markov switching model (DDMS) can also exploit the persistence associated with volatility clustering. To evaluate the contribution of duration dependence, we compare with a benchmark Markov-switching-ARCH (MS-ARCH) model. The empirical distribution generated by our proposed structure is assessed using interval forecasts and density forecasts. Implications for areas of the distribution relevant to risk management are also assessed.

Keywords: time-varying transition probabilities, discrete-state volatility dynamics, time-varying hazard function

JEL Classification: C5, C22, G15

Suggested Citation

Maheu, John M. and McCurdy, Thomas H., Volatility Dynamics Under Duration-Dependent Mixing. Journal of Empirical Finance, Vol. 7, 2000, Available at SSRN: https://ssrn.com/abstract=294684

John M. Maheu

McMaster University - Michael G. DeGroote School of Business ( email )

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Thomas H. McCurdy (Contact Author)

University of Toronto - Rotman School of Management ( email )

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416-971-3048 (Fax)

HOME PAGE: http://www-2.rotman.utoronto.ca/~tmccurdy

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