This paper investigates the time between transactions on financial markets. It is assumed that the interval between transactions is a random variable and the relation- ship between the probability to observe a transaction at each instant of time and the type of the previous trade is...

This paper investigates the time between transactions on financial markets. It is assumed that the interval between transactions is a random variable and the relationship between the probability to observe a transaction at each instant of time and the type of the previous trade is investigated....

This paper investigates the time between transactions on financial markets. It is assumed that the interval between transactions is a random variable and the relation- ship between the probability to observe a transaction at each instant of time and the type of the previous trade is...

This paper investigates the time between transactions on financial markets. It is assumed that the interval between transactions is a random variable and the relationship between the probability to observe a transaction at each instant of time and the type of the previous trade is investigated....

We introduce a multivariate multiplicative error model which is driven by componentspecific observation driven dynamics as well as a common latent autoregressive factor. The model is designed to explicitly account for (information driven) common factor dynamics as well as idiosyncratic effects...

We introduce a multivariate multiplicative error model which is driven by componentspecific observation driven dynamics as well as a common latent autoregressive factor. The model is designed to explicitly account for (information driven) common factor dynamics as well as idiosyncratic effects...

We introduce a multivariate multiplicative error model which is driven by componentspecific observation driven dynamics as well as a common latent autoregressive factor. The model is designed to explicitly account for (information driven) common factor dynamics as well as idiosyncratic effects...

We introduce a multivariate multiplicative error model which is driven by componentspecific observation driven dynamics as well as a common latent autoregressive factor. The model is designed to explicitly account for (information driven) common factor dynamics as well as idiosyncratic effects...

In this paper, we propose a framework for the modelling of multivariate dynamic processes which are driven by an unobservable common autoregressive component. Economically motivated by the mixture-of-distribution hypothesis, we model the multivariate intraday trading process of return...

We model high-frequency trading processes by a multivariate multiplicative error model that is driven by component-specific observation driven dynamics as well as a common latent autoregressive factor. The model is estimated using efficient importance sampling techniques. Applying the model to...