  Regime switching volatility models provide a tractable method of modelling
stochastic volatility. Currently the most popular method of regime switching
calibration is the Hamilton filter. We propose using the Baum-Welch algorithm,
an established technique from Engineering, to calibrate regime switching models
instead. We demonstrate the Baum-Welch algorithm and discuss the significant
advantages that it provides compared to the Hamilton filter. We provide
computational results of calibrating the Baum-Welch filter to S&P 500 data and
validate its performance in and out of sample.
