• Media type: E-Book
  • Title: Nonparametric Inference for VaR, CTE, and Expectile with High-order Precision
  • Contributor: Shen, Zhiyi [Author]; Liu, Yukun [Other]; Weng, Chengguo [Other]
  • Published: [S.l.]: SSRN, [2019]
  • Extent: 1 Online-Ressource (34 p)
  • Language: English
  • DOI: 10.2139/ssrn.3317868
  • Identifier:
  • Origination:
  • Footnote: In: The North American Actuarial Journal, In press
    Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments January 17, 2019 erstellt
  • Description: Value-at-Risk (VaR) and Conditional Tail Expectation (CTE) are the two most frequently applied risk measures in quantitative risk management. Recently, expectile has also attracted much attention as a risk measure due to its elicitability property. This paper establishes empirical likelihood based estimation with high-order precision for these three risk measures. The superiority of the estimation is justified both in theory and via simulation studies. Extensive simulation studies confirm that our method signifificantly improves the coverage probabilities for interval estimation of the three risk measures, compared to three competing methods available in the literature
  • Access State: Open Access