Modeling the volatility of financial markets is central in risk management. A seminal contribution in this field was the development of the GARCH model by Bollerslev (1986) where the volatility is a function of past asset returns. The GARCH model is today a widespread tool in risk management. However, recent studies show that estimates of GARCH models can be biased by structural breaks in the volatility dynamics (Bauwens et al., 2010; Bauwens et al., 2014). These structural breaks typically occur during periods of financial turmoil. Estimating a GARCH model on data displaying a structural break yields a non-stationary estimated model and implies poor risk predictions. A way to cope with this problem is provided by Markov-switching GARCH models (MSGARCH) whose parameters vary over time according to some regimes. These models can quickly adapt to variations in the unconditional volatility level, which improves risk predictions (see Ardia, 2008).