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

Larbi Ait-Hennani, Zoulikha Kaid, Ali Laksaci and Mustapha Rachdi ()
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Larbi Ait-Hennani: Department of Statistic and Informatics, IUT, Lille 2 University, Rond-point de l’Europe, BP. 557, F 59060 Roubaix, France
Zoulikha Kaid: Department of Mathematics, College of Science, King Khalid University, Abha 62529, Saudi Arabia
Ali Laksaci: Department of Mathematics, College of Science, King Khalid University, Abha 62529, Saudi Arabia
Mustapha Rachdi: Laboratoire AGEIS EA 7407, Université Grenoble Alpes (France), UFR SHS, BP. 47, CEDEX 09, F 38040 Grenoble, France

Mathematics, 2022, vol. 10, issue 23, 1-23

Abstract: 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 proprieties of this estimator are established under weak dependency condition. Precisely, we assume that the observations are generated from quasi-associated functional time series and we prove the almost complete convergence of the constructed estimator. This asymptotic result is obtained under a standard condition of functional time series analysis. The finite sample performance of this estimator is evaluated using artificial data.

Keywords: functional data; complete convergence (a.co.); risk analysis; expected shortfall regression; kernel method; bandwidth parameter; financial time series; quasi-associated process (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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