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Ruin Probability

Stefan Simons

ISBN 10: 3639319869 / ISBN 13: 9783639319866
Published by VDM Verlag, 2011
New Condition: New Soft cover
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128 pages. Dimensions: 8.7in. x 5.9in. x 0.3in.Ruin probability is a central component of actuarial science. The first part of this thesis describes the classical model including some premium principles and derives some main results, such as the Upper Lundberg bound and the Cramr-Lundberg approximation formula. One assumption for these results is the existence of the adjustment coefficient. Heavy tailed distribution functions are treated in the second part, where it is shown that this coefficient does not exist. Then some results from the classical model are extended to a class of heavy tailed distribution functions, i. e. subexponential functions. A central limit theorem for stable distribution functions is shown. Regularly and slowly varying functions as well as mean excess functions are explained. The third part describes some dependency structures, with a focus on copula functions, and explains the simulation procedure. First, the classical model is simulated using three different distribution functions: a light tailed, a medium tailed and a heavy tailed function. Following this, bivariate dependent claims are assumed, which are modeled with different copula functions: with and without tail dependency. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Bookseller Inventory # 9783639319866

Bibliographic Details

Title: Ruin Probability

Publisher: VDM Verlag

Publication Date: 2011

Binding: Paperback

Book Condition: New

Book Type: Paperback

About this title

Synopsis:

Ruin probability is a central component of actuarial science. The first part of this thesis describes the classical model including some premium principles and derives some main results, such as the Upper Lundberg bound and the Cramér-Lundberg approximation formula. One assumption for these results is the existence of the adjustment coefficient. Heavy tailed distribution functions are treated in the second part, where it is shown that this coefficient does not exist. Then some results from the classical model are extended to a class of heavy tailed distribution functions, i.e. subexponential functions. A central limit theorem for stable distribution functions is shown. Regularly and slowly varying functions as well as mean excess functions are explained. The third part describes some dependency structures, with a focus on copula functions, and explains the simulation procedure. First, the classical model is simulated using three different distribution functions: a light tailed, a medium tailed and a heavy tailed function. Following this, bivariate dependent claims are assumed, which are modeled with different copula functions: with and without tail dependency.

About the Author:

Stefan Simons was born in Heidelberg in 1978. After a banking apprenticeship, he studied Mathematical Finance at the University of Constance. During his studies, he gained practical experience in the reinsurance industry. Following this, he started his professional career as an insurance mathematician with Munich RE.

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