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

Abdou Ka Diongue and Dominique Guegan ()
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Dominique Guegan: CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement

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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: Whittle estimation; Long memory; Gegenbauer polynomial; heteeroskedasticity; conditional sum of squares; Whittle estimation.; Processus longue mémoire; hétéroscédasticité; estimation; Whittle. (search for similar items in EconPapers)
Date: 2008-01
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00235179v1
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Citations: View citations in EconPapers (2)

Published in 2008

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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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