Current models for predicting volatility do not incorporate information flow and are solely based on historical volatilities. We suggest a method to quantify the semantic content of words in news articles about a company and use this as a predictor of its stock volatility. The results show that...

We explore the relation between institutional quality, trust and stock-market participation. In our theoretical model, agents update their beliefs in a Bayesian manner based on observations on frauds and choose whether to invest in the stock market. The corresponding empirical model shows that...

We analyze the importance of different types of asset holdings for the interdependence of the yield curves in the EMU using a spatial VAR model. We find that the cross-border holdings of long-term debt and bank lending are important for the interdependence. Our analysis of cross-sectional...

We suggest that banks contribute extensively to systemic risk only if they are both "risky" and centrally placed in the financial network. To calculate systemic risk we apply the CoVaR measure of Adrian and Brunnermeier (2016) and measure centrality using detailed US loan syndication data. In...

We propose a bivariate component GARCH-MIDAS model to estimate the long- and short-run components of the variances and covariances. The advantage of our model to the existing DCC-based models is that it uses the same form for both the variances and covariances and that it estimates these moments...