We introduce a long memory autoregressive conditional Poisson (LMACP) model to model highly persistent time series of counts. The model is applied to forecast quoted bid-ask spreads, a key parameter in stock trading operations. It is shown that the LMACP nicely captures salient features of...

We introduce a long memory autoregressive conditional Poisson (LMACP) model to model highly persistent time series of counts. The model is applied to forecast quoted bid-ask spreads, a key parameter in stock trading operations. It is shown that the LMACP nicely captures salient features of...

We study the predictability of U.S. government bond excess returns using yield curve factors as well as yield volatility components. The yield curve factors are the level, slope and curvature factors extracted from a dynamic Nelson and Siegel (1987) framework. The yield volatility factors...

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...

Bayesian learning provides a core concept of information processing in financialmarkets. Typically it is assumed that market participants perfectly know the qual-ity of released news. However, in practice, news’ precision is rarely disclosed. Therefore, we extend standard Bayesian learning...

Trading under limited pre-trade transparency becomes increasingly popular on financial markets. We provide first evidence on traders' use of (completely) hidden orders which might be placed even inside of the (displayed) bid-ask spread. Employing TotalView-ITCH data on order messages at NASDAQ,...

An important claim of Bayesian learning and a standard assumption in price discovery models is that the strength of the price impact of unanticipated information depends on the precision of the news. In this paper, we test for this assumption by analyzing intra-day price responses of CBOT T-bond...