The paper proposes a self-exciting asset pricing model that takes into account co-jumps between prices and volatility and self-exciting jump clustering. We employ a Bayesian learning approach to implement real-time sequential analysis. We find evidence of self-exciting jump clustering since the 1987 market crash, and its importance becomes more obvious at the onset of the 2008 global financial crisis. We also find that learning affects the tail behaviors of the return distributions and has important implications for risk management, volatility forecasting, and option pricing.

We propose a novel method to extract textual information about macro fundamentals. The method has two pillars, a set of pre-defined regular expressions and a Bayesian feature selection model. We apply our technique to a 2007–2022 Reuters news corpus from Factiva to create news indices of country fundamentals. Compared to several literature alternatives, we find our method to better identify and discriminate among fundamentals based on both (i) observed economic surprises (macro announcements compared to Bloomberg survey expectations) and (ii) labels on a manually classified test sample. In an application that investigates the determinants of sovereign credit spreads, we show that including our news indices next to traditional macro variables significantly raises the explanatory power attributed to fundamentals. We also show that part of the covariance between sovereign spreads and the VIX and US high yield indices is related to global fundamentals captured by our indices.

Published in Review of Financial Studies https://doi.org/10.1093/rfs/hhu078