3 résultats pour « Reinsurance »

Sample Average Approximation for Portfolio Optimization under CVaR constraint in an (re)insurance context

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“We consider optimal allocation problems with Conditional Value-At-Risk (CVaR) constraint. We prove, under very mild assumptions, the convergence of the Sample Average Approximation method (SAA) applied to this problem, and we also exhibit a convergence rate and discuss the uniqueness of the solution. These results give (re)insurers a practical solution to portfolio optimization under market regulatory constraints, i.e. a certain level of risk.”
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Reinsurance with Neural Networks

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The study explores an insurance company managing financial risk through reinsurance, aiming to optimize terminal wealth and minimize ruin probability. Using neural networks, it finds the optimal reinsurance strategy based on expected utility and a modified Gerber-Shiu function, illustrated by a numerical example involving a Cramér-Lundberg surplus model.
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Reinsurance with neural networks

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“We consider an insurance company which faces financial risk in the form of insurance claims and market-dependent surplus fluctuations. The company aims to simultaneously control its terminal wealth (e.g. at the end of an accounting period) and the ruin probability in a finite time interval by purchasing reinsurance… We solve the problem of finding the optimal reinsurance strategy and the corresponding maximal target functional via neural networks.”
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