In this paper we present final results of a research on the riskiness of a life policies portfolio with “dread disease” (briefly: DD) coverage in additional or early form, referring to the insurer’s point of view. By means of first and second moments of the random present value of gain on a single policy, portfolio ruin probability’s estimates are derived under either Cebicev-Cantelli’s inequality or normal distribution hypothesis, in presence of a low positive correlation among risks and also introducing optimal strategies referred to a surplus reinsurance treaty to face the correlation’s effects. By means of numerical applications based on insurance company’s experiences we show the riskiness and the ruin probability considerably increase when a binary correlation exists and we demonstrate the inefficiency of the EEC minimum solvency margin rule, even in presence of optimal reinsurance strategies.

The riskiness and the reinsurance in life portfolio with Dread-Disease coverage

CARDONA, ELENA;
2005

Abstract

In this paper we present final results of a research on the riskiness of a life policies portfolio with “dread disease” (briefly: DD) coverage in additional or early form, referring to the insurer’s point of view. By means of first and second moments of the random present value of gain on a single policy, portfolio ruin probability’s estimates are derived under either Cebicev-Cantelli’s inequality or normal distribution hypothesis, in presence of a low positive correlation among risks and also introducing optimal strategies referred to a surplus reinsurance treaty to face the correlation’s effects. By means of numerical applications based on insurance company’s experiences we show the riskiness and the ruin probability considerably increase when a binary correlation exists and we demonstrate the inefficiency of the EEC minimum solvency margin rule, even in presence of optimal reinsurance strategies.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11588/101747
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