Retention and Reinsurance Programmes

Centeno, Maria de Lourdes

doi:10.1002/9780470012505.tar028

Cited by 3 publications

(2 citation statements)

References 35 publications

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“…Other approaches rely on criteria related to adjustment coefficients [5,7] or utility theory [6]. For a comprehensive review see [8]. A limitation of most of those approaches is that they focus on the primary insurer while the point of view of the reinsurer is taken into account only by simplified premium principles, often limited to the expected value or variance premium principle, i.e.…”

Section: Introductionmentioning

confidence: 99%

Sharing Risk – An Economic Perspective

Kull¹

2009

ASTIN Bull.

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We revisit the relative retention problem originally introduced by de Finetti using concepts recently developed in risk theory and quantitative risk management. Instead of using the Variance as a risk measure we consider the Expected Shortfall (Tail-Value-at-Risk) and include capital costs and take constraints on risk capital into account. Starting from a risk-based capital allocation, the paper presents an optimization scheme for sharing risk in a multi-risk class environment. Risk sharing takes place between two portfolios and the pricing of risktransfer reflects both portfolio structures. This allows us to shed more light on the question of how optimal risk sharing is characterized in a situation where risk transfer takes place between parties employing similar risk and performance measures. Recent developments in the regulatory domain ('risk-based supervision') pushing for common, insurance industry-wide risk measures underline the importance of this question. The paper includes a simple nonlife insurance example illustrating optimal risk transfer in terms of retentions of common reinsurance structures.

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Section: Introductionmentioning

confidence: 99%

Sharing Risk – An Economic Perspective

Kull¹

2009

ASTIN Bull.

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“…Then, the parameters of the translated gamma process are calculated according to (7). Hence, by using these parameters, the loading factorθ is calculated by (11), and then, we integrate this loading factor into (8). The smallest initial surplus that makes the ruin probability equal to 0.01 is calculated by using the one-dimensional optimization technique, which searches the interval from lower to upper for a minimum or maximum value of (8) in R programming.…”

Section: Numerical Analysismentioning

confidence: 99%

Optimal Retention Level for Infinite Time Horizons under MADM

Karageyik

Şahın

2016

Risks

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Abstract:In this paper, we approximate the aggregate claims process by using the translated gamma process under the classical risk model assumptions, and we investigate the ultimate ruin probability. We consider optimal reinsurance under the minimum ultimate ruin probability, as well as the maximum benefit criteria: released capital, expected profit and exponential-fractional-logarithmic utility from the insurer's point of view. Numerical examples are presented to explain how the optimal initial surplus and retention level are changed according to the individual claim amounts, loading factors and weights of the criteria. In the decision making process, we use The Analytical Hierarchy Process (AHP) and The Technique for Order of Preference by Similarity to ideal Solution (TOPSIS) methods as the Multi-Attribute Decision Making methods (MADM) and compare our results considering different combinations of loading factors for both exponential and Pareto individual claims.

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