CAViaR
提出条件自回归风险价值（CAViaR）模型，用自回归过程刻画风险价值（VaR）随时间的变化，并用分位数回归估计参数；同时引入动态分位数检验来评估模型是否恰当，实证数据支持该方法的有效性。
Value at risk (VaR) is the standard measure of market risk used by financial institutions. Interpreting the VaR as the quantile of future portfolio values conditional on current information, the conditional autoregressive value at risk (CAViaR) model specifies the evolution of the quantile over time using an autoregressive process and estimates the parameters with regression quantiles. Utilizing the criterion that each period the probability of exceeding the VaR must be independent of all the past information, we introduce a new test of model adequacy, the dynamic quantile test. Applications to real data provide empirical support to this methodology.