Fractionally integrated generalized autoregressive conditional heteroskedasticity
The new class of Fractionally Integrated Generalized AutoRegressive Conditionally Heteroskedastic (FIGARCH) processes is introduced. The conditional variance of the process implies a slow hyperbolic rate of decay for the influence of lagged squared innovations. Unlike I(d) processes for the mean, Maximum Likelihood Estimates (MLE) of the FIGARCH parameters are argued to be T1/2-consistent. The small-sample behavior of an approximate MLE procedure is assessed through a simulation study, which also documents how the estimation of a standard GARCH model tends to produce integrated, or IGARCH, like estimates. An empirical example with daily Deutschmark-U.S. dollar exchange rates illustrates the practical relevance of the new FIGARCH specification.
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- Econometrics
- 4905 Statistics
- 3802 Econometrics
- 3801 Applied economics
- 1403 Econometrics
- 1402 Applied Economics
- 0104 Statistics
Citation
Published In
DOI
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Econometrics
- 4905 Statistics
- 3802 Econometrics
- 3801 Applied economics
- 1403 Econometrics
- 1402 Applied Economics
- 0104 Statistics