Multivariate dynamic intensity peaks‐over‐threshold models
提出一个多元动态强度峰值超阈值模型，同时捕捉极端事件发生和规模的时变聚集性，应用于三大股指日收益率，发现考虑聚集效应和反馈能改进风险预测。
Summary We propose a multivariate dynamic intensity peaks‐over‐threshold model to capture extremes in multivariate return processes. The random occurrence of extremes is modeled by a multivariate dynamic intensity model, while temporal clustering of their size is captured by an autoregressive multiplicative error model. Applying the model to daily returns of three major stock indexes yields strong empirical support for a temporal clustering of both the occurrence and the size of extremes. Backtesting value‐at‐risk and expected shortfall forecasts shows that the consideration of clustering effects and of feedback between the magnitudes and the intensity of extremes results in better forecasts of risk.