CMPH: a multivariate phase-type aggregate loss distribution

Ren, Jiandong; Zitikis, Ričardas

doi:10.1515/demo-2017-0018

Cited by 4 publications

(1 citation statement)

References 36 publications

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“…The link between derivatives of pgfs and conditional distributions is not new. See, for instance, the use of derivatives to study conditional distributions with Poisson rvs [Subrahmaniam, 1966, Kocherlakota, 1988 or with phase-type distributions [Ren and Zitikis, 2017]. In a bivariate setting, [Kocherlakota, 1992] show that the conditional pgf of X 1 given the sum S = X 1 + X 2 = s is…”

Section: Discussionmentioning

confidence: 99%

Generating function method for the efficient computation of expected allocations

Blier-Wong¹,

Cossette²,

Marceau³

2022

Preprint

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Consider a risk portfolio with aggregate loss random variable S = X 1 + • • • + X n defined as the sum of the n individual losses X 1 , . . . , X n . The expected allocation, E[X i × 1 {S=k} ], for i = 1, . . . , n and k ∈ N, is a vital quantity for risk allocation and risk-sharing. For example, one uses this value to compute peer-to-peer contributions under the conditional mean risk-sharing rule and capital allocated to a line of business under the Euler risk allocation paradigm. This paper introduces an ordinary generating function for expected allocations, a power series representation of the expected allocation of an individual risk given the total risks in the portfolio when all risks are discrete. First, we provide a simple relationship between the ordinary generating function for expected allocations and the probability generating function. Then, leveraging properties of ordinary generating functions, we reveal new theoretical results on closed-formed solutions to risk allocation problems, especially when dealing with Katz or compound Katz distributions. Then, we present an efficient algorithm to recover the expected allocations using the fast Fourier transform, providing a new practical tool to compute expected allocations quickly. The latter approach is exceptionally efficient for a portfolio of independent risks.

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