CAViaR models for Value-at-Risk and Expected Shortfall with long range dependency features
研究了条件自回归分位数模型的替代设定，在分位数过程中加入慢变成分和不同期限的聚合收益作为回归变量，用10个股指数据验证了这些特征能更好捕捉尾部动态。
Abstract We consider alternative specifications of conditional autoregressive quantile models to estimate Value-at-Risk and Expected Shortfall. The proposed specifications include a slow moving component in the quantile process, along with aggregate returns from heterogeneous horizons as regressors. Using data for 10 stock indices, we evaluate the performance of the models and find that the proposed features are useful in capturing tail dynamics better.