Regime Switching Volatility Calibration by the Baum-Welch Method

TL;DR

This paper introduces the Baum-Welch algorithm as an alternative to the Hamilton filter for calibrating regime switching volatility models, demonstrating its advantages through empirical analysis of S&P 500 data.

Contribution

It proposes applying the Baum-Welch algorithm for regime switching volatility calibration, offering a novel approach compared to traditional methods.

Findings

Baum-Welch algorithm effectively calibrates regime switching models.

The method shows improved performance in and out of sample.

Empirical results on S&P 500 data validate the approach.

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 the Baum-Welch filter to S&P 500 data and validate its performance in and out of sample.