Nonparametric Inference for VaR, CTE, and expectile with high-order precision

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<dc:creator>Shen, Zhiyi</dc:creator>
<dc:creator>Liu, Yukun</dc:creator>
<dc:creator>Weng, Chengguo</dc:creator>
<dc:date>2019-09-02</dc:date>
<dc:description xml:lang="es">Sumario: This article 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.</dc:description>
<dc:identifier>https://documentacion.fundacionmapfre.org/documentacion/publico/es/bib/170002.do</dc:identifier>
<dc:language>eng</dc:language>
<dc:rights xml:lang="es">InC - http://rightsstatements.org/vocab/InC/1.0/</dc:rights>
<dc:subject xml:lang="es">Gerencia de riesgos</dc:subject>
<dc:subject xml:lang="es">Cálculo actuarial</dc:subject>
<dc:subject xml:lang="es">Modelos predictivos</dc:subject>
<dc:subject xml:lang="es">Análisis de riesgos</dc:subject>
<dc:type xml:lang="es">Artículos y capítulos</dc:type>
<dc:title xml:lang="es">Nonparametric Inference for VaR, CTE, and expectile with high-order precision</dc:title>
<dc:relation xml:lang="es">En: North American actuarial journal. - Schaumburg : Society of Actuaries, 1997- = ISSN 1092-0277. - 02/09/2019 Tomo 23 Número 3 - 2019 , p. 364-385</dc:relation>
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