Statistical inference for a relative risk measure

Yi He, Yanxi Hou, Liang Peng, Jiliang Sheng

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Abstract

For monitoring systemic risk from regulators' point of view, this paper 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. 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.
Original languageEnglish
Pages (from-to)301-311
Number of pages11
JournalJournal of Business & Economic Statistics
Volume31
Issue number2
DOIs
Publication statusPublished - 2019

Keywords

  • Copula
  • expected shortfall
  • jackknife empirical likelihood
  • nonparametric estimation
  • systemic risk

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