We propose a new approach to measuring the effect of unobservable private information or beliefs on volatility. Using high-frequency intraday data, we estimate the volatility effect of a well identified shock on the volatility of the stock returns of large European banks as a function of 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. Bid and ask returns are decomposed into a common ("efficient return") factor and two market-side-specific components...

We study the effectiveness of climate change policy in a model with multiple non-renewable resources that differ in their carbon content. We find that, when allowing some time between announcement and implementation of a cap on carbon dioxide emissions, emissions from non-renewable energy...

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

What role does noise play in equity markets? Answering this question usually leads immediately to specifying a model of fundamentals and hence the pervasive joint hypothesis quagmire. We avoid this dilemma by measuring noise volatility directly by focusing on the behavior of country closed-end...