Stationary parameterization of GARCH processes

Tucker McElroy ()
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Tucker McElroy: U.S. Census Bureau

Economics Bulletin, 2022, vol. 42, issue 4, 1908 - 1930

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.

Keywords: Conditional Heteroscedasticity; Stationarity; Time Series (search for similar items in EconPapers)
JEL-codes: C1 C5 (search for similar items in EconPapers)
Date: 2022-12-30
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