Persistence in Variance, Structural Change, and the GARCH Model

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 failur...

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Bibliographic Details
Published in:Journal of business & economic statistics 1990-04, Vol.8 (2), p.225-234
Main Authors: Lamoureux, Christopher G., Lastrapes, William D.
Format: Article
Language:English
Subjects:
Bootstrap
Business structures
Corporations
Economic models
Half lives
I-GARCH
Integration in variance
Manycompanies
Mathematical models
Modeling
Monte Carlo
Monte Carlo methods
Monte Carlo simulation
Price variance
Securities markets
Simulations
Standard deviation
Statistical variance
Stochastic models
Stock prices
Studies
Time series
Variance analysis
Volatility
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Summary: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.
ISSN:0735-0015
1537-2707
DOI:10.1080/07350015.1990.10509794