Statistical Inference for a Relative Risk Measure
提出一种对市场联动敏感的相对风险度量，并给出非参数估计量的渐近正态性及基于平滑估计的Jackknife经验似然置信区间方法，适用于独立观测和AR-GARCH模型数据，有助于监管机构监测系统性风险。
For monitoring systemic risk from regulators’ point of view, this article proposes a relative risk measure, which is sensitive to the market comovement. The asymptotic normality of a nonparametric estimator and its smoothed version is established when the observations are independent. To effectively construct an interval without complicated asymptotic variance estimation, a jackknife empirical likelihood inference procedure based on the smoothed nonparametric estimation is provided with a Wilks type of result in case of independent observations. When data follow from AR-GARCH models, the relative risk measure with respect to the errors becomes useful and so we propose a corresponding nonparametric estimator. A simulation study and real-life data analysis show that the proposed relative risk measure is useful in monitoring systemic risk.