Conditional Extremes in Asymmetric Financial Markets

The global financial crisis of 2007–2009 revealed the great extent to which systemic risk can jeopardize the stability of the entire financial system. An effective methodology to quantify systemic risk is at the heart of the process of identifying the so-called systemically important financial institutions for regulatory purposes as well as to investigate key drivers of systemic contagion. The article proposes a method for dynamic forecasting of CoVaR, a popular measure of systemic risk. As a first step, we develop a semi-parametric framework using asymptotic results in the spirit of extreme value theory (EVT) to model the conditional probability distribution of a bivariate random vector given that one of the components takes on a large value, taking into account important features of financial data such as asymmetry and heavy tails. In the second step, we embed the proposed EVT method into a dynamic framework via a bivariate GARCH process. An empirical analysis is conducted to demonstrate and compare the performance of the proposed methodology relative to a very flexible fully parametric alternative.

2007至2009年的全球金融危机暴露了系统性风险足以严重危及整个金融体系稳定的程度。有效的系统性风险量化方法，是监管层面识别所谓系统重要性金融机构（systemically important financial institutions），以及探究系统性传染关键驱动因素的核心环节。本文提出了一种用于系统性风险常用测度——条件在险价值（CoVaR）的动态预测方法。首先，本文基于极值理论（extreme value theory, EVT）的渐近结果构建半参数框架，对二元随机向量的条件概率分布进行建模，即当其中一个分量取极端值时的条件分布，同时兼顾金融数据的非对称性与厚尾性等重要特征。第二步，本文借助二元广义自回归条件异方差（GARCH）过程，将所提出的EVT方法融入动态建模框架。本文开展实证分析，以验证并对比所提方法相较于极具灵活性的全参数化替代方案的性能表现。