Tail Moments of Compound Distributions
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| Tag | 1 | 2 | Valor |
|---|---|---|---|
| LDR | 00000cab a2200000 4500 | ||
| 001 | MAP20220023733 | ||
| 003 | MAP | ||
| 005 | 20220915131714.0 | ||
| 008 | 220915e20220912esp|||p |0|||b|spa d | ||
| 040 | $aMAP$bspa$dMAP | ||
| 084 | $a7 | ||
| 100 | $0MAPA20080653613$aRen, Jiandong | ||
| 245 | 1 | 0 | $aTail Moments of Compound Distributions$cJiandong Ren |
| 520 | $aIn this article, we study the moment transform of both univariate and multivariate compound sums. We first derive simple explicit formulas for the first and second moment transforms when the (loss) frequency distribution is in the so-called (a,b,0) class. Then we show that the derived formulas can be used to efficiently compute risk measures such as the tail conditional expectation (TCE), the tail variance (TV), and higher tail moments. The results generalize those in Denuit (North American Actuarial Journal, 24 (4):51232, 2020). | ||
| 650 | 4 | $0MAPA20080579258$aCálculo actuarial | |
| 650 | 4 | $0MAPA20080620752$aVariables macro-económicas | |
| 773 | 0 | $wMAP20077000239$g12/09/2022 Tomo 26 Número 3 - 2022 , p. 336-350$x1092-0277$tNorth American actuarial journal$dSchaumburg : Society of Actuaries, 1997- |