Markov switching in exchange rate models: will more regimes help?

This paper examines the performance of Markov switching models of the exchange rate using a data-driven approach to determine the number of regimes rather than simply assuming two states. The analysis is conducted for the British pound, Canadian dollar, and Japanese yen exchange rates against the US dollar over the last 30 years with alternative specifications including a simple segmented trends model and Markov switching autoregressive models with monetary fundamentals. A noteworthy finding is that the number of regimes that minimizes mean square forecast errors tends to correspond to the number of regimes selected by Bayesian information criteria (but not Markov-switching-specific information criteria). For the monetary models, the number of regimes that minimizes forecast errors also tends to correspond to the most parsimonious model with well-behaved residuals. Although allowing for more regimes yields forecasting improvement over single- or two-regime models, the Markov switching model is still unable to outperform a random walk. This suggests that exchange rate models need to allow for novel, as opposed to repetitive or predetermined, structural change.