A new algorithm for the loss distribution function with applications to Operational Risk Management
Operational risks inside banks and insurance companies is currently an important task. The computation of a risk measure associated to these risks lies on the knowledge of the so-called Loss Distribution Function. Traditionally this distribution function is computed via the Panjer algorithm which is an iterative algorithm. In this paper, we propose an adaptation of this last algorithm in order to improve the computation of convolutions between Panjer class distributions and continuous distributions. This new approach permits to reduce drastically the variance of the estimated VAR associated to the operational risks.
|Date of creation:||Nov 2009|
|Date of revision:|
|Publication status:||Published in Documents de travail du Centre d'Economie de la Sorbonne 2009.23 - ISSN : 1955-611X. 2009|
|Note:||View the original document on HAL open archive server: https://halshs.archives-ouvertes.fr/halshs-00384398v2|
|Contact details of provider:|| Web page: https://hal.archives-ouvertes.fr/|
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- Philippe Artzner & Freddy Delbaen & Jean-Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228.
- Mark Craddock & David Heath & Eckhard Platen, 1999. "Numerical Inversion of Laplace Transforms: A Survey of Techniques with Applications to Derivative Pricing," Research Paper Series 27, Quantitative Finance Research Centre, University of Technology, Sydney.
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