This paper proposes an auxiliary particle filter algorithm for inference in regime switching stochastic volatility models in which the regime state is governed

We study a Markov switching stochastic volatility model with heavy tail innovations in the observable process. Due to the economic interpretation of the hidden

This work deals with multivariate stochastic volatility models, which account for a time-varying variance-covariance structure of the observable variables. We f

This paper is concerned with simulation based inference in generalized models of stochastic volatility defined by heavy-tailed student-t distributions (with unk