12 papers
Central limit theory for serial tail dependence estimators in heavy-tailed long memory linear time series
Ioan Scheffel, Marco Oesting, Gilles Stupfler
We prove multiple central limit theorems for serial tail dependence estimators in heavy-tailed long memory linear time series. The main theoretical tools are two novel multivariate…
Regularized geometric quantiles and universal linear distribution functionals
Dimitri Konen, Gilles Stupfler
Geometric quantiles are popular location functionals to build rank-based statistical procedures in multivariate settings. They are obtained through the minimization of a non-smooth…
Central limit theory for Peaks-over-Threshold partial sums of long memory linear time series
Ioan Scheffel, Marco Oesting, Gilles Stupfler
Over the last 30 years, extensive work has been devoted to developing central limit theory for partial sums of subordinated long memory linear time series. A much less studied prob…
Asymptotic Properties of Generalized Shortfall Risk Measures for Heavy-tailed Risks
Tiantian Mao, Gilles Stupfler, Fan Yang
We study a general risk measure called the generalized shortfall risk measure, which was first introduced in Mao and Cai (2018). It is proposed under the rank-dependent expected ut…
Extreme expectile estimation for short-tailed data, with an application to market risk assessment
Abdelaati Daouia, Simone A. Padoan, Gilles Stupfler
The use of expectiles in risk management has recently gathered remarkable momentum due to their excellent axiomatic and probabilistic properties. In particular, the class of elicit…
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…
Tail risk inference via expectiles in heavy-tailed time series
Anthony C. Davison, Simone A. Padoan, Gilles Stupfler
Expectiles define the only law-invariant, coherent and elicitable risk measure apart from the expectation. The popularity of expectile-based risk measures is steadily growing and t…
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…
An Offspring of Multivariate Extreme-Value Theory: The Max-Characteristic Function
Michael Falk, Gilles Stupfler
This paper introduces max-characteristic functions (max-CFs), which are an offspring of multivariate extreme-value theory. A max-CF characterizes the distribution of a random vecto…
Uniform strong consistency of a frontier estimator using kernel regression on high order moments
Stephane Girard, Armelle Guillou, Gilles Stupfler
We consider the high order moments estimator of the frontier of a random pair introduced by Girard, S., Guillou, A., Stupfler, G. (2012). {\it Frontier estimation with kernel regre…