Author
Listed:
- Denuit, Michel
- Ortega-Jiménez, Patricia
- Robert, Christian-Yann
Abstract
The conditional expectation $m_{X}(s)=\mathrm{E}[X|S=s]$ , where X and Y are two independent random variables with $S=X+Y$ , plays a key role in various actuarial applications. For instance, considering the conditional mean risk-sharing rule, $m_X(s)$ determines the contribution of the agent holding the risk X to a risk-sharing pool. It is also a relevant function in the context of risk management, for example, when considering natural capital allocation principles. The monotonicity of $m_X(\!\cdot\!)$ is particularly significant under these frameworks, and it has been linked to log-concave densities since Efron (1965). However, the log-concavity assumption may not be realistic in some applications because it excludes heavy-tailed distributions. We consider random variables with regularly varying densities to illustrate how heavy tails can lead to a nonmonotonic behavior for $m_X(\!\cdot\!)$ . This paper first aims to identify situations where $m_X(\!\cdot\!)$ could fail to be increasing according to the tail heaviness of X and Y. Second, the paper aims to study the asymptotic behavior of $m_X(s)$ as the value s of the sum gets large. The analysis is then extended to zero-augmented probability distributions, commonly encountered in applications to insurance, and to sums of more than two random variables and to two random variables with a Farlie–Gumbel–Morgenstern copula. Consequences for risk sharing and capital allocation are discussed. Many numerical examples illustrate the results.
Suggested Citation
Denuit, Michel & Ortega-Jiménez, Patricia & Robert, Christian-Yann, 2025.
"Conditional expectations given the sum of independent random variables with regularly varying densities,"
ASTIN Bulletin, Cambridge University Press, vol. 55(2), pages 449-485, May.
Handle:
RePEc:cup:astinb:v:55:y:2025:i:2:p:449-485_11
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