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Title: Statistical inference for a relative risk measure Authors:  Yi He - Monash University (Australia)
Liang Peng - Georgia State University (United States)
Yanxi Hou - Georgia Institute of Technology (United States)
Jiliang Sheng - Jiangxi University of Finance and Economics (China)
Yi He - University of Amsterdam (Netherlands) [presenting]
Abstract: For monitoring systemic risk from regulators' point of view, a relative risk measure is proposed, 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. In order 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.