Network Effects on Risk Co-Movements : A Network Quantile Autoregression-Based Analysis

The connections and volatilities of financial institutions are important features in studies on the financial market. Based on the tail-event-driven networks reflecting the risk interdependence between financial institutions, we introduce quantile regression to achieve the dynamic ranking of systemic importance among institutions. This article investigates the interdependence from joint extreme movements based on bivariate extreme value theory and classification analysis. Furthermore, we propose a new ranking strategy regarding the influence of institutions based on the network effects and quantile regression technique. We carry out a series of methods on systemically important financial institutions to show their efficiency and real-world insights