Estimation of k-Factor GIGARCH Process: A Monte Carlo Study |
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| Authors: | Abdou Kâ Diongue Dominique Guégan |
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| Institution: | 1. UFR SAT, Université Gaston Berger, Saint-Louis, Sénégal, and School of Economics and Finance , Queensland University of Technology , Brisbane, Australia diongue@ces.ens-cachan.fr;3. Centre Economique de la Sorbonne (CES) and Paris School of Economics (PSE) , MSE – Université Paris 1 Panthéon-Sorbonne , Paris, France |
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| Abstract: | In this article, 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, using four different conditional distribution functions. |
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| Keywords: | Conditional sum of squares Gegenbauer polynomial Heteroskedasticity Long memory Whittle estimation |
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