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Nonparametric Extreme Conditional Expectile Estimation by Stéphane Girard; Gilles Stupfler; Antoine Usseglio‐Carleve is a Mathematics article available to read on EtoBox.

What is Nonparametric Extreme Conditional Expectile Estimation about?

Expectiles and quantiles can both be defined as the solution of minimization problems. Contrary to quantiles though, expectiles are determined by tail expectations rather than tail probabilities, and define a coherent risk measure. For these two reasons in particular, expectiles have recently started to be considered as serious candidates to become standard tools in actuarial and financial risk management. However, expectiles and their sample versions do not benefit from a simple explicit form, making their analysis significantly harder than that of quantiles and order statistics. This difficulty is compounded when one wishes to integrate auxiliary information about the phenomenon of interest through a finite-dimensional covariate, in which case the problem becomes the estimation of conditional expectiles. In this paper, we exploit the fact that the expectiles of a distribution F are in fact the quantiles of another distribution E explicitly linked to F, in order to construct nonparametric kernel estimators of extreme conditional expectiles. We analyze the asymptotic properties of our estimators in the context of conditional heavy-tailed distributions. Applications to simulated dat

Who reads Nonparametric Extreme Conditional Expectile Estimation?

Author

Stéphane Girard; Gilles Stupfler; Antoine Usseglio‐Carleve

Publisher

John Wiley and Sons; Wiley (Blackwell Publishing); Blackwell Publishing Inc.; Wiley (ISSN 0303-6898)

Published

2020

Language

EN

Field

Mathematics (Physical Sciences)