Estimation of k-Factor GIGARCH Process: A Monte Carlo Study

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 Wh...

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Bibliographic Details
Published in:Communications in statistics. Simulation and computation 2008-10, Vol.37 (10), p.2037-2049
Main Authors: Diongue, Abdou Kâ, Guégan, Dominique
Format: Article
Language:English
Subjects:
Computer Science
Conditional sum of squares
Distribution theory
Economics and Finance
Exact sciences and technology
Gegenbauer polynomial
Heteroskedasticity
Humanities and Social Sciences
Long memory
Mathematics
Modeling and Simulation
Numerical analysis
Numerical analysis. Scientific computation
Numerical methods in probability and statistics
Probability and statistics
Probability theory and stochastic processes
Sciences and techniques of general use
Statistics
Statistics Theory
Whittle estimation
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Summary: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.
ISSN:0361-0918
1532-4141
DOI:10.1080/03610910802304994