We revisit the role of time in measuring the price impact of trades using a new empirical method that combines spread decomposition and dynamic duration modeling. Previous studies which have addressed the issue in a vector-autoregressive framework conclude that times when markets are most active...

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 recent years support vector regression (SVR), a novel neural network (NN) technique, has been successfully used for financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH method is proposed and is compared with a moving...

Using monthly data for the period 19532003, we apply a real-time modeling approach to investigate the implications of U.S. political stock market anomalies for forecasting excess stock returns. Our empirical findings show that political variables, selected on the basis of widely used model...