Inference for the tail conditional allocation: Large sample properties, insurance risk assessment, and compound sums of concomitants

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Inference for the tail conditional allocation: Large sample properties, insurance risk assessment, and compound sums of concomitants. / Грибкова, Надежда Викторовна; Su, Jianxi; Zitikis, Ričardas.

In: Insurance: Mathematics and Economics, Vol. 107, 01.11.2022, p. 199-222.

Research output: Contribution to journal › Article › peer-review

BibTeX

@article{e6a594887cbf49ef9fcd839a5031d95e,

title = "Inference for the tail conditional allocation: Large sample properties, insurance risk assessment, and compound sums of concomitants",

abstract = "We derive consistency, asymptotic normality, and standard error estimation for the tail conditional allocation, also known as the marginal expected shortfall, under minimal conditions and thus geared toward widest applicability. These advances have become possible due to a newly developed technique that hinges on compound sums of concomitants. An insurance inspired numerical study has been designed to illustrate the performance of the obtained results",

keywords = "Capital allocations, Marginal expected shortfall, Compound sums, Order statistics, Concomitants, Capital allocations, Marginal expected shortfall, Compound sums, Order statistics, Concomitants",

author = "Грибкова, {Надежда Викторовна} and Jianxi Su and Ri{\v c}ardas Zitikis",

note = "Publisher Copyright: {\textcopyright} 2022 Elsevier B.V.",

year = "2022",

month = nov,

day = "1",

doi = "10.1016/j.insmatheco.2022.08.009",

language = "English",

volume = "107",

pages = "199--222",

journal = "Insurance: Mathematics and Economics",

issn = "0167-6687",

publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Inference for the tail conditional allocation: Large sample properties, insurance risk assessment, and compound sums of concomitants

AU - Грибкова, Надежда Викторовна

AU - Su, Jianxi

AU - Zitikis, Ričardas

PY - 2022/11/1

Y1 - 2022/11/1

N2 - We derive consistency, asymptotic normality, and standard error estimation for the tail conditional allocation, also known as the marginal expected shortfall, under minimal conditions and thus geared toward widest applicability. These advances have become possible due to a newly developed technique that hinges on compound sums of concomitants. An insurance inspired numerical study has been designed to illustrate the performance of the obtained results

AB - We derive consistency, asymptotic normality, and standard error estimation for the tail conditional allocation, also known as the marginal expected shortfall, under minimal conditions and thus geared toward widest applicability. These advances have become possible due to a newly developed technique that hinges on compound sums of concomitants. An insurance inspired numerical study has been designed to illustrate the performance of the obtained results

KW - Capital allocations

KW - Marginal expected shortfall

KW - Compound sums

KW - Order statistics

KW - Concomitants

KW - Capital allocations

KW - Marginal expected shortfall

KW - Compound sums

KW - Order statistics

KW - Concomitants

UR - http://www.scopus.com/inward/record.url?scp=85137656997&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/ae4a28e3-4ef3-39b0-9cf0-a4d14ca0b360/

U2 - 10.1016/j.insmatheco.2022.08.009

DO - 10.1016/j.insmatheco.2022.08.009

M3 - Article

VL - 107

SP - 199

EP - 222

JO - Insurance: Mathematics and Economics

JF - Insurance: Mathematics and Economics

SN - 0167-6687

ER -

ID: 99441256