Optimal reinsurance under risk and uncertainty

Balbás, Alejandro; Balbás, Beatriz; Balbás, Raquel; Heras, Antonio

doi:10.1016/j.insmatheco.2014.11.001

Cited by 51 publications

(41 citation statements)

References 23 publications

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“…Researchers [12][13][14] examined the problem of risk and uncertainty that both insurer and reinsurer are facing. Reinsurance helps insurance companies to take very large and expensive risks.…”

Section: Brief Literature Reviewmentioning

confidence: 99%

Improvement of the methodology for assessing the capital adequacy to cover the reinsurance default risk

Pukała¹,

Vnukova²,

Achkasova³

et al. 2017

EA-XXI

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Abstract. Introduction. The development of the insurance market in Ukraine is closely associated with the instruments used by domestic insurers to manage low capitalisation, including coinsurance and reinsurance services. Reinsurance helps insurance companies to take very large and expensive risks. Thus, the questions of generalisation of both national and international experience in default risk assessment of reinsurers in an insurance company and provision of recommendations to ensure reliability of activity of insurance companies are relevant. The purpose of the paper is to provide recommendations to improve capital adequacy assessment concerning the coverage of the reinsurance default risk. Methodology. The methodological basis of this study is the theory of insurance and insurance concepts. Scale measurement of capital adequacy of insurance companies relevant to the coverage of the reinsurance default risk was conducted by using methods of mathematical statistics. Results. Based on the example of 50 Ukrainian insurance companies, we have conducted scale measurement of capital adequacy to cover the default risk of reinsurers for the years of 2015-2016. The originality of the research lies in the improvement of capital adequacy assessment concerning the coverage of the reinsurance default risk, which, unlike the existing methodology, considers the designed scale and criteria of the capital adequacy indicator of the insurance company to cover the reinsurance default risk at «inadequate», «adequate», «medium», «good» and «excellent» levels. Conclusions. The practical value of the paper lies in the proposed approaches to improve capital adequacy assessment concerning the coverage of the reinsurance default risk, which will enhance the level of reinsurance and facilitate the fulfillment of obligations by insurance companies. Keywords: Reinsurance Default Risk; Methodology; Scale Measurement; Capital Adequacy; Insurance Company JEL Classification: F36; F38; F65; G22; G28 DOI: https://doi.org/10.21003/ea.V165-30 Пукала Р. кандидат економічних наук, проректор у справах студентів, Ярославська державна вища техніко-економічна школа ім. Броніслава Маркевича, Ярослав, Польща Внукова Н. М. доктор економічних наук, професор, завідувач кафедри управління фінансовими послугами, Харківський національний економічний університет імені Семена Кузнеця, Харків, Україна Ачкасова С. А. кандидат економічних наук, доцент, кафедра управління фінансовими послугами, Харківський національний економічний університет імені Семена Кузнеця, Харків, Україна Смоляк В. А. кандидат економічних наук, доцент, кафедра управління фінансовими послугами, Харківський національний економічний університет імені Семена Кузнеця, Харків, Україна Удосконалення методики оцінки достатності капіталу на покриття ризику дефолту перестраховика Анотація. У статті удосконалено методику оцінки достатності капіталу на покриття можливих збитків у разі настання ризику дефолту перестраховика, яка, на відміну від існуючих, враховує розроблені шкали та критерії для показни...

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Section: Brief Literature Reviewmentioning

confidence: 99%

Improvement of the methodology for assessing the capital adequacy to cover the reinsurance default risk

Pukała¹,

Vnukova²,

Achkasova³

et al. 2017

EA-XXI

View full text Add to dashboard Cite

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“…Finally, we would like to mention the recent work of Balbas et al [33] who consider ambiguity on the side of both the insurer and the insured 1 . The authors assume that both parties behave according to the maxmin expected utility (MEU) model of Gilboa and Schmeidler [9], that is their ambiguity is described by a collection of additive priors (a set of probability measures), and preferences are aggregated using the minimum value of the usual (Lebesgue) integral over this collection of priors.…”

Section: Related Literaturementioning

confidence: 99%

Optimal Insurance for a Minimal Expected Retention: The Case of an Ambiguity-Seeking Insurer

Amarante

Ghossoub

2015

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract: In the classical expected utility framework, a problem of optimal insurance design with a premium constraint is equivalent to a problem of optimal insurance design with a minimum expected retention constraint. When the insurer has ambiguous beliefs represented by a non-additive probability measure, as in Schmeidler, this equivalence no longer holds. Recently, Amarante, Ghossoub and Phelps examined the problem of optimal insurance design with a premium constraint when the insurer has ambiguous beliefs. In particular, they showed that when the insurer is ambiguity-seeking, with a concave distortion of the insured's probability measure, then the optimal indemnity schedule is a state-contingent deductible schedule, in which the deductible depends on the state of the world only through the insurer's distortion function. In this paper, we examine the problem of optimal insurance design with a minimum expected retention constraint, in the case where the insurer is ambiguity-seeking. We obtain the aforementioned result of Amarante, Ghossoub and Phelps and the classical result of Arrow as special cases. Terms of use: Documents in

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“…This is no longer the case when the main aim is to find the best strategy within an optimisation problem, where finding the "best" model does not guarantee a robust decision, which is the main aim of the modelling process. A standard way to achieve this is to use the method of robust optimisation; comprehensive surveys could be found, for example, in Ben- Tal literature could be found in Balbás et al (2015) and Asimit et al (2017).…”

Section: Introductionmentioning

confidence: 99%

Optimal Robust Insurance with a Finite Uncertainty Set

Asimit

Xie

2018

SSRN Journal

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Decision-makers who usually face model/parameter risk may prefer to act prudently by identifying optimal contracts that are robust to such sources of uncertainty. In this paper, we tackle this issue under a finite uncertainty set that contains a number of probability models that are candidates for the "true", but unknown model. Various robust optimisation models are proposed, some of which are already known in the literature, and we show that all of them could be efficiently solved. The numerical experiments are run for various risk preference choices and it is found that for relatively large sample size, the modeler should focus on finding the best possible fit for the unknown probability model in order to achieve the most robust decision. If only small samples are available, then the modeler should consider two robust optimisation models, namely the Weighted Average Model or Weighted Worst-case Model, rather than focusing on statistical tools aiming to estimate the probability model. Amongst those two, the better choice of the robust optimisation model depends on how much interest the modeler puts on the tail risk when defining its objective function. These findings suggest that one should be very careful when robust optimal decisions are sought in the sense that the modeler should first understand the features of its 1 Corresponding author. Phone: +861064494702. Fax: +861064495024. 1 2 objective function and the size of the available data, and then to decide whether robust optimisation or statistical inferences is the best practical approach.

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Optimal reinsurance under risk and uncertainty

Cited by 51 publications

References 23 publications

Improvement of the methodology for assessing the capital adequacy to cover the reinsurance default risk

Improvement of the methodology for assessing the capital adequacy to cover the reinsurance default risk

Optimal Insurance for a Minimal Expected Retention: The Case of an Ambiguity-Seeking Insurer

Optimal Robust Insurance with a Finite Uncertainty Set

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