Hidden Markov Models can be considered as an extension of mixture models, which allows for dependent observations and makes them suitable for financial applications. In a hierarchical Bayesian framework, we show how reversible jump Markov chain Monte Carlo techniques can be used to estimate the parameters of the model, as well as the number of regimes. An application to exchange rate dynamics modeling is presented
