Mean–variance efficient strategies in proportional reinsurance under group correlation in a gaussian framework
| Autor: | Paolo Serafini, Laura Ziani, Flavio Pressacco |
|---|---|
| Přispěvatelé: | Ziani, Laura, Dept. of Finance, University of Udine, Italy (DIFI), Dept. of Finance, University of Udine, Italy, Dipartimento di Matematica e Informatica - Universita Udine (DIMI), Università degli Studi di Udine - University of Udine [Italie], Dept. of Finance, University of Udine |
| Rok vydání: | 2011 |
| Předmět: |
Statistics and Probability
Reinsurance Economics and Econometrics Mathematical optimization group correlation Group (mathematics) Mathematical finance Gaussian Mathematics::Optimization and Control Constrained optimization [SHS.ECO]Humanities and Social Sciences/Economics and Finance proportional reinsurance Space (mathematics) Mean-Variance efficiency group correlation Mean-Variance efficiency constrained quadratic optimization proportional reinsurance group correlation Set (abstract data type) symbols.namesake symbols constrained quadratic optimization Quadratic programming Statistics Probability and Uncertainty [SHS.ECO] Humanities and Social Sciences/Economics and Finance Mathematics |
| Zdroj: | European Actuarial Journal. 1:433-454 |
| ISSN: | 2190-9741 2190-9733 |
| DOI: | 10.1007/s13385-011-0020-6 |
| Popis: | Accepted for publication on European Actuarial Journal, first number, 2010; The paper concerns optimal mean-variance proportional reinsurance under group correlation. In order to solve the corresponding constrained quadratic optimization problem, we make large recourse both to the smart friendly technique originally proposed by B. de Finetti in his pioneering paper and to the well known Karush-Kuhn-Tucker conditions for constrained optimization. We offer closed form results and insightful considerations about the problem. In detail, we give closed form formulas to express the efficient mean-variance retention set both in the retention space and in the mean-variance one. |
| Databáze: | OpenAIRE |
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