We analyze how markets adjust to new information when the reliability of news is uncertain and has to be estimated itself. We propose a Bayesian learning model where market participants receive fundamental information along with noisy estimates of news’ precision. It is shown that the...

We study the impact of the arrival of macroeconomic news on the informational and noise-driven components in high-frequency quote processes and their conditional variances. We decompose bid and ask returns into a common ("efficient return") factor and two market-side-specific components...

Recent findings indicate that macroeconomic survey forecasts are anchoring biased and therefore are inefficient. However, despite highly significant test coefficients, a bias adjustment does not improve forecasts' quality. We find that the cognitive bias is a statistical artifact because the...

In den letzten Jahren ist der Handel in Zinsderivaten, vor allem Zinsterminkontrakten und Zinsswaps, rasch gewachsen. Für die Banken, die den Handel von Zinsderivaten in erster Linie tragen, folgt daraus ein erhebliches Risiko. Dieses hat in jüngster Zeit immer wieder zu Befürchtungen...

We propose a novel approach to model serially dependent positive-valued variables which realize a non-trivial proportion of zero outcomes. This is a typical phenomenon in financial time series observed at high frequencies, such as cumulated trading volumes. We introduce a flexible point-mass...

We propose a methodology for forecasting the systemic impact of financial institutions in interconnected systems. Utilizing a five-year sample including the 2008/9 financial crisis, we demonstrate how the approach can be used for the timely systemic risk monitoring of large European banks and...