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Structural Change as an Alternative to Long Memory in Financial Time Series

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

5 Scopus citations

Abstract

This paper shows that volatility persistence in GARCH models and spurious long memory in autoregressive models may arise if the possibility of structural changes is not incorporated in the time series model. It also describes a tractable hidden Markov model (HMM) in which the regression parameters and error variances may undergo abrupt changes at unknown time points, while staying constant between adjacent change-points. Applications to real and simulated financial time series are given to illustrate the issues and methods.

Original languageEnglish
Title of host publicationEconometric Analysis of Financial and Economic Time Series
EditorsThomas Fomby, Dek Terrell
Pages205-224
Number of pages20
DOIs
StatePublished - 2006

Publication series

NameAdvances in Econometrics
Volume20 PART 2
ISSN (Print)0731-9053

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