Stochastic Models in Life Insurance [electronic resource] / by Michael Koller.

Por: Koller, Michael [author.]Tipo de material: TextoTextoSeries EAA SeriesEditor: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2012Descripción: XI, 219p. 41 illus., 33 illus. in color. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783642284397Trabajos contenidos: SpringerLink (Online service)Tema(s): Mathematics | Distribution (Probability theory) | Economics -- Statistics | Mathematics | Probability Theory and Stochastic Processes | Game Theory, Economics, Social and Behav. Sciences | Statistics for Business/Economics/Mathematical Finance/InsuranceFormatos físicos adicionales: Sin títuloClasificación CDD: 519.2 Clasificación LoC:QA273.A1-274.9QA274-274.9Recursos en línea: de clik aquí para ver el libro electrónico
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Springer eBooksResumen: The book provides a sound mathematical base for life insurance mathematics and applies the underlying concepts to concrete examples. Moreover the models presented make it possible to model life insurance policies by means of Markov chains. Two chapters covering ALM and abstract valuation concepts on the background of Solvency II complete this volume. Numerous examples and a parallel treatment of discrete and continuous approaches help the reader to implement the theory directly in practice.
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1. A general life insurance model -- 2. Stochastic processes -- 3. Interest rate -- 4. Cash flows and the mathematical reserve -- 5. Difference equations and differential equations -- 6. Examples and problems from applications -- 7. Hattendorff's Theorem -- 8. Unit-linked policies -- 9. Policies with stochastic interest rate -- 10. Technical analysis -- 11. Abstract valuation -- 12. Policyholder bonus mechanism -- A. Notes on stochastic integration -- B. Examples -- C. Mortality rates in Germany -- D. Mortality rates in Switzerland -- E. Java code for the calculation of the Markov model -- References -- Notation -- Index.

The book provides a sound mathematical base for life insurance mathematics and applies the underlying concepts to concrete examples. Moreover the models presented make it possible to model life insurance policies by means of Markov chains. Two chapters covering ALM and abstract valuation concepts on the background of Solvency II complete this volume. Numerous examples and a parallel treatment of discrete and continuous approaches help the reader to implement the theory directly in practice.

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