Working paper

Tail expectile-VaR estimation in the semiparametric Generalized Pareto model

Yasser Abbas, Abdelaati Daouia, Boutheina Nemouchi, and Gilles Stupfler

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Abstract

Expectiles have received increasing attention as coherent and elicitable market risk measure. Their estimation from heavy-tailed data in an extreme value framework has been studied using solely the Weissman extrapolation method. We challenge this dominance by developing the theory of two classes of semiparametric Generalized Pareto estimators that make more efficient use of tail observations by incorporating the location, scale and shape extreme value parameters: the first class relies on asymmetric least squares estimation, while the second is based on extreme quantile estimation. A comparison with simulated and real data shows the superiority of our proposals for real-valued profit-loss distributions.

Keywords

Expectile, Extreme risk, Generalized Pareto model, Heavy tails, Semiparametric; extrapolation;

JEL codes

  • C13: Estimation: General
  • C14: Semiparametric and Nonparametric Methods: General
  • C18: Methodological Issues: General
  • C53: Forecasting and Prediction Methods • Simulation Methods
  • C58: Financial Econometrics

Reference

Yasser Abbas, Abdelaati Daouia, Boutheina Nemouchi, and Gilles Stupfler, “Tail expectile-VaR estimation in the semiparametric Generalized Pareto model”, TSE Working Paper, n. 25-1607, January 2025.

See also

Published in

TSE Working Paper, n. 25-1607, January 2025