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Bivariate mixture models have been used to explain the stochastic behavior of daily price changes and trading volume on financial markets. In this class of models, price changes and volume follow a mixture of bivariate distributions with the unobservable number of price-relevant information...
Persistent link: https://www.econbiz.de/10005532375
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...
Persistent link: https://www.econbiz.de/10011097552
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...
Persistent link: https://www.econbiz.de/10011097560
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...
Persistent link: https://www.econbiz.de/10011097573
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...
Persistent link: https://www.econbiz.de/10011097605
Using a novel four-phase model based upon a conditional autoregressive Wishart framework for realized variances and covariances we quantify intra-daily volatility spillovers within and across the US, German and Japanese stock markets before and during the subprime crisis. We find significant...
Persistent link: https://www.econbiz.de/10011263954
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...
Persistent link: https://www.econbiz.de/10010825879