Volatility Dynamics under DurationDependent Mixing

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 stan-dard rst-order Markov switching 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 can also exploit the persistence associated with volatil-ity clustering. To evaluate the contribution of duration dependence, we compare with a benchmark Markov switching 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 for risk management are also assessed. We are indebted to two anonymous referees and the Editor, Franz Palm, for very helpful suggestions. We are also grateful to: participants at the Limberg Institute o