Abstract (EN)

We study a Markov switching stochastic volatility model with heavy tail innovations in the observable process. Due to the economic interpretation of the hidden volatility regimes, these models have many nancial applications like asset allocation, option pricing and risk management. The Markov switching process is able to capture clustering e ects and jumps in volatility. Heavy tail innovations account for extreme variations in the observed process. Accurate modelling of the tails is important when estimating quantiles is the major interest like in risk management applications. Moreover we follow a Bayesian approach to ltering and estimation, focusing on recently developed simulation based ltering techniques, called Particle Filters. Simulation based lters are recursive techniques, which are useful when assuming non-linear and non-Gaussian latent variable models and when processing data sequentially. They allow to update parameter estimates and state ltering as new observations become available.