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...

This paper presents an empirical evaluation of recently proposed asset pricing models which extend the standard preference specification by a reference level of consumption. We motivate an alternative model that accounts for the return on human capital as a determinant of the reference level....

Dufour and Engle (2000) have shown that the duration between subsequent trade events carries informational content with respect to the evolution of the fundamental asset value. Their analysis supports the notion that "no trade means no information" derived from Easley and O'Hara's (1992)...

This paper considers the dynamics of spot and futures prices in the presence of arbitrage. A partially linear error correction model is proposed where the adjustment coefficient is allowed to depend non-linearly on the lagged price difference. The model is estimated using data on the DAX index...

We reconsider the issue of price discovery in spot and futures markets. We use a threshold error correction model to allow for arbitrage opportunities to have an impact on the return dynamics. We estimate the model using quote midpoints, and we modify the model to account for time-varying...

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 efficiency...

The taxation of dividends in Germany underwent major changes. We analyze the implications of these changes for the valuation of DAX futures contracts and test the resulting hypotheses empirically. We find that dividend taxation cannot explain the level of deviations from the cost-of-carry...

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. Bid and ask returns are decomposed into a common ("efficient return") factor and two market-side-specific components...

Bayesian learning provides the core concept of information processing in financial markets. Typically, it is assumed that market participants know perfectly the quality of released news. However, in practice, news' precision is rarely disclosed. Therefore, we extend standard Bayesian learning,...

Post-earnings-announcement drift (PEAD) is one of the most solidly documented asset pricing anomalies. We use the controlled conditions of an experimental lab to investigate whether earnings autocorrelation is the driving cause of this anomaly. We observe PEAD in settings with uncorrelated and...