We propose an iterative procedure to efficiently estimate models with complex log-likelihood functions and the number of parameters relative to the observations being potentially high. Given consistent but inefficient estimates of sub-vectors of the parameter vector, the procedure yields...

We propose an iterative procedure to efficiently estimate models with complex log-likelihood functions and the number of parameters relative to the observations being potentially high. Given consistent but inefficient estimates of sub-vectors of the parameter vector, the procedure yields...

We propose an iterative procedure to efficiently estimate models with complex log-likelihood functions and the number of parameters relative to the observations being potentially high. Given consistent but inefficient estimates of sub-vectors of the parameter vector, the procedure yields...

We propose an iterative procedure to efficiently estimate models with complex log-likelihood functions and the number of parameters relative to the observations being potentially high. Given consistent but inefficient estimates of sub-vectors of the parameter vector, the procedure yields...

We propose an iterative procedure to efficiently estimate models with complex log-likelihood functions and the number of parameters relative to the observations being potentially high. Given consistent but inefficient estimates of sub-vectors of the parameter vector, the procedure yields...

We propose an iterative procedure to efficiently estimate models with complex log-likelihood functions and the number of parameters relative to the observations being potentially high. Given consistent but inefficient estimates of sub-vectors of the parameter vector, the procedure yields...

This paper delineates the simultaneous impact of non-anticipated information onmean and variance of the intraday return process by including appropriate variablesaccounting for the news flow into both the mean and the variance function. This allowsus to differentiate between the consistent price...

It is well documented that the U.S. employment report has a strong price impact in financial markets. Based of these precision proxies, we show that prices respond significantly stronger to more precise information, even after controlling for an asymmetric price response to ’good’ and ’bad’...

Preise auf Kapitalmärkten werden durch nicht-antizipierte Informationen getrieben. Eine zentrale Aussage Baysianischer Lernmodelle impliziert, dass die Stärke der Preisreaktion einerseits von der Höhe der nicht antizipierten Komponente abhängt, andererseits aber auch von der Präzision der...