This paper introduces a new information density indicator to provide a more comprehensive understanding of price reactions to news and, more specifically, to the sources of jumps in financial markets. Our information density indicator, which measures the abnormal amount of noisy “ticker”...

We empirically explore the role of investor disagreement and news for various types of stock jumps, where the dynamic of stock returns is captured by a contagion model. Our disagreement measure is derived from more than 173 million tweets from a social media investing platform, StockTwits, for...

We develop a new variational Bayes estimation method for large-dimensional sparse vector autoregressive models with exogenous predictors. Unlike existing Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms, our approach is not based on a structural form representation of the...

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...