Dynamic Bivariate Peak Over Threshold Model for Joint Tail Risk Dynamics of Financial Markets
提出一个动态双变量超阈值模型，同时建模边际和联合尾部风险的时变行为，引入可解释的尾部关联度指标，发现全球股市同洲市场联合尾部风险高且危机时关联度上升，并开发基于联合尾部风险最小化的投资组合优化方法。
We propose a novel dynamic bivariate peak over threshold (PoT) model to study the time-varying behavior of joint tail risk in financial markets. The proposed framework provides simultaneous modeling for dynamics of marginal and joint tail risk, and generalizes the existing tail risk literature from univariate dimension to multivariate dimension. We introduce a natural and interpretable tail connectedness measure and examine the dynamics of joint tail behavior of global stock markets: empirical evidence suggests markets from the same continent have time-varying and high-level joint tail risk, and tail connectedness increases during periods of crisis. We further enrich the tail risk literature by developing a novel portfolio optimization procedure based on bivariate joint tail risk minimization, which gives promising risk-rewarding performance in backtesting.