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Abstract

We extend the Regime Switching for Dynamic Correlations (RSDC) model by Pelletier (Journal of Econometrics, 2006), to determine the effect of underlying fundamental variables in the evolution of the dynamic correlations between multiple time series. By introducing state dependent transition probabilities to the switching process between different regimes - governed by a Markov chain, we are able to identify potential thresholds and spillover effects in the dynamic process. In addition, asymmetric correlations between the series are determined. We simulate data for multiple series and find an initial better fit of state dependent transition probabilities, versus constant transition probabilities, for the regime switching model. Capturing more precisely the dynamic interrelationships between multiple series or markets conveys many benefits including - potential efficiency gains from related operations, determining the effects of shocks from related variables, as well as improvement in hedging operations.

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