Statistical Inference for a Relative Risk Measure

Yi He, Yanxi Hou, Liang Peng and Jiliang Sheng

Journal of Business & Economic Statistics, 2019, vol. 37, issue 2, 301-311

Abstract: 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.

Date: 2019
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DOI: 10.1080/07350015.2017.1321549

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