Bivariate mixture models have been used to explain the stochastic behavior of daily price changes and trading volume on fmancial markets. In this class of models price changes and volume follow a mixture of bivariate distributions with the unobservable number of price relevant information...

This paper investigates the Information content of daily trading volume with respect to the long-run or high persistent and the short-run or transitory components of the volatility of daily stock market returns using bivariate mixture models. For this purpose, the Standard bivariate mixture...

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...

This paper develops a systematic Markov Chain Monte Carlo (MCMC) framework based upon Efficient Importance Sampling (EIS) which can be used for the analysis of a wide range of econometric models involving integrals without an analytical solution. EIS is a simple, generic and yet accurate...

Empirical evidence suggests a sharp volatility decline of the growth in U.S. gross domestic product (GDP) in the mid-1980s. Using Bayesian methods, we analyze whether a volatility reduction can also be detected for the German GDP. Since statistical inference for volatility processes critically...