Quantile Connectedness: Modeling Tail Behavior in the Topology of Financial Networks
提出一种通过分位数回归估计含共同因子误差结构的向量自回归模型的新技术，用于研究17个主权国家及其金融部门间的信用风险溢出，发现尾部风险传播远强于均值或中位数，并开发了衡量左右尾相对溢出强度的指标，可用于风险管理和监测。
We develop a new technique to estimate vector autoregressions with a common factor error structure by quantile regression. We apply our technique to study credit risk spillovers among a group of 17 sovereigns and their respective financial sectors between January 2006 and December 2017. We show that idiosyncratic credit risk shocks propagate much more strongly in both tails than at the conditional mean or median. Furthermore, we develop a measure of the relative spillover intensity in the right and left tails of the conditional distribution that provides a timely aggregate measure of systemic financial fragility and that can be used for risk management and monitoring purposes. This paper was accepted by Gustavo Manso, finance.