Asymptotics of CoVaR inference in two-quantile-regression

原文摘要

Measuring systemic risk has been an important topic in recent research on risk management. Adrian and Brunnermeier (2016) pioneered the CoVaR measure for systemic risk, which is defined as the conditional quantile of system loss given some state predictors and one stress event of an individual loss at a certain risk level. They proposed to estimate CoVaR by a two-quantile-regression approach, which is simple and easy to implement in practice, but without stating or developing the necessary asymptotic theory. In this paper, we develop tests to reveal that using the classical regularity condition in quantile regressions for the two-quantile-regression model may be unrealistic by revisiting the datasets in Adrian and Brunnermeier (2016). Under the proposed weaker conditions, we further develop the asymptotic theory of the CoVaR inference and introduce a random weighted bootstrap method to quantify the inference uncertainty. We examine the finite sample performance of the proposed tests in terms of size and power, as well as the coverage probabilities of confidence intervals for CoVaR. Finally, we demonstrate the CoVaR inference across financial institutions and time.

摘要来源：RePEc · 出版社摘要