4 papers
Optimal pooling and distributed inference for the tail index and extreme quantiles
Abdelaati Daouia, Simone A. Padoan, Gilles Stupfler
This paper investigates pooling strategies for tail index and extreme quantile estimation from heavy-tailed data. To fully exploit the information contained in several samples, we…
GARCH-UGH: A bias-reduced approach for dynamic extreme Value-at-Risk estimation in financial time series
Hibiki Kaibuchi, Yoshinori Kawasaki, Gilles Stupfler
The Value-at-Risk (VaR) is a widely used instrument in financial risk management. The question of estimating the VaR of loss return distributions at extreme levels is an important…
Joint inference on extreme expectiles for multivariate heavy-tailed distributions
Simone A. Padoan, Gilles Stupfler
The notion of expectiles, originally introduced in the context of testing for homoscedasticity and conditional symmetry of the error distribution in linear regression, induces a la…
On a class of norms generated by nonnegative integrable distributions
Michael Falk, Gilles Stupfler
We show that any distribution function on with nonnegative, nonzero and integrable marginal distributions can be characterized by a norm on , calle…