Estimating conditional tail expectation with actuarial applications in view

By Vytaras Brazauskas, B. Jones, Madan Lal Puri, Ricardas Zitikis. 2008

119 citations recorded by the source corpus.

Subfields: Demography, Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/e156daf18c5b79dc1b995131f89ad6747bcd3740

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Citations in the corpus

Statistical estimation of composite risk functionals and risk optimization problems (2015, methodology): “We refer to Jones and Zitikis (2003, 2007), Brazauskas et al. (2008), Beutner and Zähle (2010), Tsukahara (2013), Belomestny and Krätschmer (2012) for more details on this approach.”
A New Approach to Assessing Model Risk in High Dimensions (2015, unknown): “For example, Cont (2006) found bounds on prices of contingent claims, incorporating model risk on the choice of the risk neutral measure used for pricing. In the same spirit, Kerkhof, Melenberg, and Schumacher (2010) assessed model risk in the context of management of market risk by computing the wo”
$L$-functions, processes, and statistics in measuring economic inequality and actuarial risks (2009, background): “One of such generalizations is the nested L-statistic, called also the NL-statistic, introduced by Brazauskas, Jones, Puri and Zitikis [10].”
Non‐parametric Estimation of Extreme Risk Measures from Conditional Heavy‐tailed Distributions (2014, background): “In such a case, the Karamata representation (Bingham et al., 1987, theorem 1.3.1) of the slowly varying function can be written as `a.yjx/ D ca.x/ exp Z y 1 "a.ujx/ u du ; (8) where ca. / is a positive function and "a.yjx/ !”
A joint quantile and expected shortfall regression framework (2017, unknown): “The same holds true for the efficiency of the sample ES estimators (based on the sample quantile) of Brazauskas et al. (2008) and Chen (2008). Remark 2.11 (Pseudo-R2 and the choice of a(Y)). By choosing a(Y ) = αG1(Y ) + G2(Y ) in (2.2), we can guarantee non-negative losses ρ(Y, X, θ) ≥ 0. This choi”