Optimal time-varying tail risk network with a rolling window approach

Abstract

We contribute to the literature on financial network contagion and systemic risk by developing a time-varying framework based on the rolling window technic and high dimensional quantile regression. The new selection criterion enables us to determine the optimal rolling width, which trades off the estimation accuracy and time variation of the tail risk network. Monte Carlo simulations show that our procedure significantly improves upon the estimation performance of the time-varying model. Using Chinese banking’s market data over 2009–2019, we measure the time-varying tail risk connectedness among banks using idiosyncratic returns. We find strong evidence of prominent risk dependence across banks during the stock crash. The network model using the optimal rolling window outperforms the traditional approaches in capturing structural changes.