We propose a dynamic factor model for the analysis of multivariate time series count data. Our model allows for idiosyncratic as well as common serially correlated latent factors in order to account for potentially complex dynamic interdependence between series of counts. The model is estimated...

We propose a dynamic factor model for the analysis of multivariate time series count data. Our model allows for idiosyncratic as well as common serially correlated latent factors in order to account for potentially complex dynamic interdependence between series of counts. The model is estimated...

In this paper we consider ML estimation for a broad class of parameter-driven models for discrete dependent variables with spatial correlation. Under this class of models, which includes spatial discrete choice models, spatial Tobit models and spatial count data models, the dependent variable is...

Financial market spillovers around the globeThis paper investigates the transmission of return and volatility spillovers around the globe. It draws on index futures of three representative indices, namely the Dow Jones Euro Stoxx 50, the S&P 500 and the Nikkei 225. Devolatised returns and...

The aim of this study is to provide a comprehensive description of the dependence pattern of stock returns by studying a range of quantiles of the conditional return distribution using quantile autoregression. This enables us in particular to study the behavior of extreme quantiles associated...

Financial market spillovers around the globeThis paper investigates the transmission of return and volatility spillovers around the globe. It draws on index futures of three representative indices, namely the Dow Jones Euro Stoxx 50, the S&P 500 and the Nikkei 225. Devolatised returns and...