Estimation of k-factor GIGARCH process: a Monte Carlo study

Abdou Ka Diongue and Dominique Guégan
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Dominique Guégan: Centre d'Economie de la Sorbonne et Paris School of Economics, https://cv.archives-ouvertes.fr/dominique-guegan

Documents de travail du Centre d'Economie de la Sorbonne from Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne

Abstract: In this paper, we discuss the parameter estimation for a k-factor generalized long memory process with conditionally heteroskedastic noise. Two estimation methods are proposed. The first method is based on the conditional distribution of the process and the second is obtained as an extension of Whittle's estimation approach. For comparison purposes, Monte Carlo simulations are used to evaluate the finite sample performance of these estimation techniques

Keywords: Long memory; Gegenbauer polynomial; heteroskedasticity; conditional sum of squares; Whittle estimation (search for similar items in EconPapers)
JEL-codes: C53 (search for similar items in EconPapers)
Pages: 18 pages
Date: 2008-01
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Citations: View citations in EconPapers (2)

Published in Communications in Statistics - Simulation and Computation, 2008, 37, (10), pp.2037-2049

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https://hal.science/halshs-00235179 (application/pdf)
https://doi.org/10.1080/03610910802304994

Related works:
Working Paper: Estimation of k-factor GIGARCH process: a Monte Carlo study (2008)
Working Paper: Estimation of k-Factor Gigarch Process: A Monte Carlo Study (2008)
Working Paper: Estimation of k-factor GIGARCH process: a Monte Carlo study (2008)
Working Paper: Estimation of k-Factor Gigarch Process: A Monte Carlo Study (2008)
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