Most of the empirical applications of the stochatic volatility (SV) model are based on the assumption that the conditional distribution of returns given the latent volatility process is normal. In this paper the SV model based on a conditional normal distribution is compa-red with SV...

According to the bivariate mixture hypothesis (BMH) as proposed by Tauchen and Pitts (1983) and Harris (1986,1987) the daily price changes and the correspond-ing trading volume on speculative markets follow a joint mixture of distributions with the unobservable number of daily information events...

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...

According to the bivariate mixture hypothesis (BMH) as proposed by Tauchen and Pitts (1983) and Harris (1986, 1987) the daily price changes and the corresponding trading volume on speculative markets follow a joint mixture of distributions with the unobservable number of daily information events...

Most of the empirical applications of the stochastic volatility (SV) model are based on the assumption that the conditional distribution of returns, given the latent volatility process, is normal. In this paper, the SV model based on a conditional normal distribution is compared with SV...

We employ a bivariate common factor model to establish a permanent-transitory decomposition of two major stock indices (the Deutsche Aktienindex (DAX) for Germany and the Dow Jones Industrial Average (DJIA) for the United States). Using high-frequency data, we (1) identify a common trend shared...

type="main" xml:id="jtsa12078-abs-0001"The parameters of integer autoregressive models with Poisson, or negative binomial innovations can be estimated by maximum likelihood where the prediction error decomposition, together with convolution methods, is used to write down the likelihood function....