Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
Estimating the VaR-induced Euler allocation rule. / Gribkova, N.V. ; Su, Jianxi; Zitikis, Ricardas.
в: ASTIN Bulletin, Том 53, № 3, 02.09.2023, стр. 619 - 635.Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
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TY - JOUR
T1 - Estimating the VaR-induced Euler allocation rule
AU - Gribkova, N.V.
AU - Su, Jianxi
AU - Zitikis, Ricardas
N1 - N.V. Gribkova, J. Su, and R. Zitikis, Estimating the VaR-induced Euler allocation rule, ASTIN Bulletin: The Journal of the IAA , First View , pp. 1 - 17 (Published online by Cambridge University Press: 02 May 2023), DOI: https://doi.org/10.1017/asb.2023.17
PY - 2023/9/2
Y1 - 2023/9/2
N2 - The prominence of the Euler allocation rule (EAR) is rooted in the fact that it is the only return on risk-adjusted capital (RORAC) compatible capital allocation rule. When the total regulatory capital is set using the value-at-risk (VaR), the EAR becomes – using a statistical term – the quantile-regression (QR) function. Although the cumulative QR function (i.e., an integral of the QR function) has received considerable attention in the literature, a fully developed statistical inference theory for the QR function itself has been elusive. In the present paper, we develop such a theory based on an empirical QR estimator, for which we establish consistency, asymptotic normality, and standard error estimation. This makes the herein developed results readily applicable in practice, thus facilitating decision making within the RORAC paradigm, conditional mean risk sharing, and current regulatory frameworks.
AB - The prominence of the Euler allocation rule (EAR) is rooted in the fact that it is the only return on risk-adjusted capital (RORAC) compatible capital allocation rule. When the total regulatory capital is set using the value-at-risk (VaR), the EAR becomes – using a statistical term – the quantile-regression (QR) function. Although the cumulative QR function (i.e., an integral of the QR function) has received considerable attention in the literature, a fully developed statistical inference theory for the QR function itself has been elusive. In the present paper, we develop such a theory based on an empirical QR estimator, for which we establish consistency, asymptotic normality, and standard error estimation. This makes the herein developed results readily applicable in practice, thus facilitating decision making within the RORAC paradigm, conditional mean risk sharing, and current regulatory frameworks.
KW - Capital allocations
KW - conditional mean risk sharing
KW - Quantile regression
KW - order statistics
KW - concomitants
KW - Capital allocations
KW - conditional mean risk sharing
KW - quantile regression
KW - order statistics
KW - concomitants
UR - https://www.mendeley.com/catalogue/e2f56655-4840-3af3-ba60-4b9c719c33b3/
U2 - 10.1017/asb.2023.17
DO - 10.1017/asb.2023.17
M3 - Article
VL - 53
SP - 619
EP - 635
JO - ASTIN Bulletin
JF - ASTIN Bulletin
SN - 0515-0361
IS - 3
ER -
ID: 105354142