A Long Memory Model with Normal Mixture GARCH

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)517-539
Journal / PublicationComputational Economics
Volume38
Issue number4
Publication statusPublished - Nov 2011
Externally publishedYes

Abstract

We present an exploratory analysis of a class of long memory models with a normal mixture generalized autoregressive conditional heteroskedasticity innovation process. Monte Carlo results are used to infer the performance of the maximum likelihood estimator. The estimation biases are associated with, amongst others, the mixing parameter, and these biases are usually insignificant. As an illustration, we fit the proposed model to four countries inflation data. It is found that the performance of the long memory model with normal mixture generalized autoregressive conditional heteroskedasticity is better than, say, both autoregressive moving average and long memory models with a standard generalized autoregressive conditional heteroskedasticity specification in terms of the flexibility to describe both the time-varying conditional skewness and kurtosis. © 2011 Springer Science+Business Media, LLC.

Research Area(s)

  • Conditional heteroskedasticity, Inflation rate, Long memory, Normal mixture

Citation Format(s)

A Long Memory Model with Normal Mixture GARCH. / Cheung, Yin-Wong; Chung, Sang-Kuck.

In: Computational Economics, Vol. 38, No. 4, 11.2011, p. 517-539.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review