Abstract
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 and comparing the performance of the Baum–Welch and the Hamilton filter to S&P 500 and Nikkei 225 data, examining their performance in and out of sample.
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