Bayesian smoothing for time-varying extremal dependence
提出一种贝叶斯时变模型，通过学习联合极值随时间变化的动态规律，应用于全球主要股票市场，揭示了过去30年极值依赖的复杂模式。
Abstract We propose a Bayesian time-varying model that learns about the dynamics governing joint extreme values over time. Our model relies on dual measures of time-varying extremal dependence, that are modelled via a suitable class of generalized linear models conditional on a large threshold. The simulation study indicates that the proposed methods perform well in a variety of scenarios. The application of the proposed methods to some of the world’s most important stock markets reveals complex patterns of extremal dependence over the last 30 years, including passages from asymptotic dependence to asymptotic independence.