A Comparison of the use of risk measures and the application of the adjustment coefficient in calculating optimal reinsurance.

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dc.contributor.authorWanja, Richard Rodrot
dc.date.accessioned2020-10-19T11:04:25Z
dc.date.available2020-10-19T11:04:25Z
dc.date.issued2018
dc.description2018 Conference paper held at Strathmore University, Nairobi Kenya. Theme (Mathematical Applications and Economics)en_US
dc.description.abstractReinsurance is a mechanism by which an insurance company can protect itself against the risk of losses by transferring the risk to other companies. A reinsurance arrangement could be considered optimal if it minimizes the probability of ruin. When an insurer effects reinsurance, they are required to pay a reinsurance premium. Therefore, the total cost to the insurer in the presence of reinsurance is the cost of meeting the retained loss in the event of a claim and paying the reinsurance premium. The purchase of reinsurance is therefore a compromise between expected gain and security. Reinsurance reduces the cedant’s risk; on the other hand, it will reduce the expected gain of the cedant. Claims experience is assumed to follow a particular loss distribution. i.e. the exponential, pareto, gamma, lognormal, Weibull and burr distributions. This paper determines optimal reinsurance by use of risk measures such as VaR and CTE. The results are compared with the use of the adjustment coefficient in determining the optimal reinsurance strategy. Claims experience data is simulated through the Monte Carlo simulation techniquesen_US
dc.description.sponsorshipStrathmore Institute of Mathematical Sciences, Strathmore University, Nairobi, Kenyaen_US
dc.identifier.urihttp://hdl.handle.net/11071/9533
dc.language.isoenen_US
dc.publisherStrathmore Universityen_US
dc.subjectOptimal reinsuranceen_US
dc.subjectSimulation techniquesen_US
dc.subjectInsuranceen_US
dc.titleA Comparison of the use of risk measures and the application of the adjustment coefficient in calculating optimal reinsurance.en_US
dc.typePresentationen_US
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2018 Research and Innovation Conference