Volatility Dynamics Under Duration-Dependent Mixing
28 Pages Posted: 18 Apr 2002 Last revised: 2 Mar 2012
Abstract
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: Suggested Citation
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