This paper studies the behaviour of crypto currencies financial time-series of which Bitcoin is the most prominent example. The dynamic of those series is quite complex displaying extreme observations, asymmetries, and several nonlinear characteristics which are difficult to model. We develop a...

We perform a large-scale empirical study to compare the forecasting performance of single-regime and Markov-switching GARCH (MSGARCH) models from a risk management perspective. We find that, for daily, weekly, and ten-day equity log-returns, MSGARCH models yield more accurate Value-at-Risk,...

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 propose a new class of models specifi cally tailored for spatio-temporal data analysis. To this end, we generalize the spatial autoregressive model with autoregressive and heteroskedastic disturbances, i.e. SARAR(1,1), by exploiting the recent advancements in Score Driven (SD) models...