Time series of counts are often characterized by high overdispersion and persistence. These extreme features challenge the existing models. We approach this problem by combining the framework of INAR with a latent Markov structure. We call it HMM-INAR since it belongs to the class of hidden...

We introduce a new Stochastic Volatility model that postulates a general correlation structure between the shocks of the measurement and log volatility equations at different temporal lags. The resulting specification is able to better characterize the leverage effect and propagation in...

This paper studies the leverage effect and its propagation over time. We show that common volatility models like the GJRGARCH, the Exponential GARCH, and the asymmetric SV can be inappropriate to correctly represent the leverage effect and its propagation for financial time series. We propose to...

In this paper we propose a new class of dynamic mixture models (DAMMs) being able to sequentially adapt the mixture components as well as the mixture composition using information coming from the data. The information driven nature of the proposed class of models allows to exactly compute the...