Tag Archives: Amsterdam

Actuarial science, Jury, Miscellaneous, Trip

Amsterdam, PhD defense

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http://freakonometrics.hypotheses.org/files/2013/01/Capture-d%E2%80%99e%CC%81cran-2013-01-21-a%CC%80-09.14.31.png

Last week, I was involved in the PhD defense of Julien Tomas, with Rob Kaas (promotor), Frédéric Planchet (co-promotor), Katrien Antonio, Marc Goovaerts, Ann De Schepper and Michel Vellekoop. The PhD thesis – untitled quantifying biometric life insurance risks with non-parametric smoothing methods – can be dowloaded on http://dare.uva.nl/… and on http://tel.archives-ouvertes.fr/.

http://freakonometrics.hypotheses.org/files/2013/01/Capture-d%E2%80%99e%CC%81cran-2013-01-21-a%CC%80-09.13.07.png

The R codes will be available soon on my blog (and on Julien’s new website http://www.likelihood.me/).

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Actuarial science, Computer, Conferences, Insurance, Jury, Trip

Amsterdam

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I will be in Amsterdam for the end of this week. I will be in the jury of the PhD defense of Julien Tomas, entitled “Quantifying Biometric Life Insurance Risks With Non-Parametric Smoothing Methods” (the thesis will probably be online soon). But before, I will give a talk at the actuarial seminar at UvA. My visit last time was a real pleasure, and it should be the same this time too. I will give a talk this Thursday on “R for actuarial science“. The slides can be downloaded from here.

Conferences, Trip

Talk on Value-at-Risk estimation

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Exposé au séminaire d’Actuariat, à la faculté de Sciences Economiques à Amsterdam (UvA). L’exposé portera sur l’estimation de quantiles (et de Value-at-Risk) sur des données de pertes, avec une application de gestion de portefeuille moyenne-VaR.

In this talk we propose several nonparametric estimators of quantiles based on Beta kernel and applied to transformed data by the generalized Champernowne distribution initially fitted to the data. A Monte-Carlo based study will show that those estimators improve the efficiency, not only for light tailed distributions, but mainly for heavy tailed, when the probability level is close to 1.Another application will be seen, on portfolio optimization in the mean-VaR context.