Estimation and model-based combination of causality networks among large US banks and insurance companies

Causality is a widely-used concept in theoretical and empirical economics. The recent financial economics literature has used the standard Granger causality to detect for the presence of contemporaneous links among financial institutions, that, in turn, determine a network structure. Subsequent studies have combined the estimated networks with traditional pricing or risk measurement models to improve their fit to empirical data. In this paper, we provide two contributions. First, we show how to use a linear factor model as a device for estimating a combination of several networks that monitor the links across variables from different viewpoints. Second, we highlight the advantages of combining quantile-based methods with the Granger causality when the focus is on risk propagation. The empirical evidence supports our contributions.