Robust estimation of Markov-switching GARCH models
The study of volatility is important in several areas of finance, and GARCH models have been widely used in the literature due to their ability to capture key stylised facts of financial time series. However, in some cases, financial time series exhibit structural changes in volatility dynamics, for which standard GARCH models may be inadequate; in such situations, Markov-switching GARCH models provide a more suitable framework. On the other hand, outliers are often present in empirical data. This paper shows that conventional estimators can be strongly affected by outliers and proposes an estimator that is more robust to their presence. The simulation results and empirical applications indicate that the proposed robust estimator is competitive and useful under contamination, but not uniformly superior to the QML-t estimator.