Title: "Non-parametric estimators for risk measures: application to loss distributions and portfolio optimization"
Abstract: 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.
The seminar will take place in E-building, see below for the address.
Paper: Beta Kernel estimation for Value-At-Risk of heavy-tailed distribution
Paper: Estimating allocations for Value-at-Risk portfolio optimzation
