Persistence in variance, structural change, and the GARCH model

Christopher G Lamoureux, William D. Lastrapes

Research output: Contribution to journalArticle

521 Citations (Scopus)

Abstract

This article examines the persistence of the variance, as measured by the generalized autoregressive conditional heteroskedasticity (GARCH) model, in stock-return data. In particular, we investigate the extent to which persistence in variance may be overstated because of the existence of, and failure to take account of, deterministic structural shifts in the model. Both an analysis of daily stock-return data and a Monte Carlo simulation experiment confirm the hypothesis that GARCH measures of persistence in variance are sensitive to this type of model misspecification.

Original languageEnglish (US)
Pages (from-to)225-234
Number of pages10
JournalJournal of Business and Economic Statistics
Volume8
Issue number2
DOIs
StatePublished - 1990
Externally publishedYes

Fingerprint

Conditional Heteroskedasticity
Structural Change
structural change
Persistence
persistence
Stock Returns
Model Misspecification
Monte Carlo Experiment
Simulation Experiment
Monte Carlo Simulation
Model
simulation
experiment
Structural change
Autoregressive conditional heteroskedasticity
Stock returns

Keywords

  • Bootstrap
  • Integration in variance
  • L-GARCH
  • Monte Carlo

ASJC Scopus subject areas

  • Statistics and Probability
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty
  • Social Sciences (miscellaneous)

Cite this

Persistence in variance, structural change, and the GARCH model. / Lamoureux, Christopher G; Lastrapes, William D.

In: Journal of Business and Economic Statistics, Vol. 8, No. 2, 1990, p. 225-234.

Research output: Contribution to journalArticle

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