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Nonparametric Estimation of the Expected Shortfall Regression for Quasi-Associated Functional Data

In this paper, we study the nonparametric estimation of the expected shortfall regression when the exogenous observation is functional. The constructed estimator is obtained by combining the double kernels estimator of both conditional value at risk and conditional density function. The asymptotic p...

Täydet tiedot

Tallennettuna:
Päätekijät: Larbi Ait-Hennani, Zoulikha Kaid, Ali Laksaci, Mustapha Rachdi
Aineistotyyppi: Artigo
Kieli:Inglês
Julkaistu: MDPI AG 2022-11-01
Sarja:Mathematics
Aiheet:
functional data
complete convergence (a.co.)
risk analysis
expected shortfall regression
kernel method
bandwidth parameter
Linkit:https://www.mdpi.com/2227-7390/10/23/4508
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