Modeling Extreme Events: Time-Varying Extreme Tail Shape
提出一个动态半参数框架来研究尾部参数的时变特征，基于广义帕累托分布建模超过阈值的极端值，并应用于美国股票日收益率和欧元区主权债券收益率变化数据。
We propose a dynamic semi-parametric framework to study time variation in tail parameters. The framework builds on the Generalized Pareto Distribution (GPD) for modeling peaks over thresholds as in Extreme Value Theory, but casts the model in a conditional framework to allow for time-variation in the tail parameters. We establish parameter regions for stationarity and ergodicity and for the existence of (unconditional) moments and consider conditions for consistency and asymptotic normality of the maximum likelihood estimator for the deterministic parameters in the model. Two empirical datasets illustrate the usefulness of the approach: daily U.S. equity returns, and 15-minute euro area sovereign bond yield changes.