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Stationary parameterization of GARCH processes

Author

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  • Tucker S McElroy

    (U.S. Census Bureau)

Abstract

We propose using the multivariate logistic transform to re-parameterize the Autoregressive Conditionally Heteroscedastic model such that the necessary stationarity constraints are automatically imposed, thereby allowing for unconstrained optimization when computing quasi-maximum likelihood estimates. A few simulations and a standard R data set of daily closing prices (Germany DAX) provide illustrations of the re-parameterization. We offer some numerical comparisons to available R packages (fgarch and rugarch), and comment on the potential advantages of the new technique.

Suggested Citation

  • Tucker S McElroy, 2022. "Stationary parameterization of GARCH processes," Economics Bulletin, AccessEcon, vol. 42(4), pages 1908-1930.
    • Handle: RePEc:ebl:ecbull:eb-21-01162
    as

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    File URL: http://www.accessecon.com/Pubs/EB/2022/Volume42/EB-22-V42-I4-P159.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    Conditional Heteroscedasticity; Stationarity; Time Series;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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