Regime Switching for Dynamic Correlations

We propose a new model for the variance between multiple time series, the Regime Switching Dynamic Correlation. We decompose the covariances into correlations and standard deviations and the correlation matrix follow a regime switching model; it is constant within a regime but different across regimes. The transitions between the regimes are governed by a Markov chain. This model does not suffer from a curse of dimensionality and it allows analytic computation of multi-step ahead conditional expectations of the variance matrix. We also present an empirical application which illustrates that our model can have a better in-sample fit of the data than the Dynamic Conditional Correlation model proposed by Engle(JBES, 2002)