Fractionally Integrated Models With ARCH Errors
Abstract: We introduce ARFIMA-ARCH models which simultaneously incorporate fractional differencing and conditional heteroskedasticity. We develop the likelihood function and a numerical estimation procedure for this model class. Two ARCH models - Engle- and Weiss-type - are explicitly treated and st...| Link(s) zu Dokument(en): | IHS Publikation |
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| Hauptverfasser: | , |
| Format: | IHS Series NonPeerReviewed |
| Sprache: | Englisch |
| Veröffentlicht: |
Institut für Höhere Studien
1993
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| Zusammenfassung: | Abstract: We introduce ARFIMA-ARCH models which simultaneously incorporate fractional differencing and conditional heteroskedasticity. We develop the likelihood function and a numerical estimation procedure for this model class. Two ARCH models - Engle- and Weiss-type - are explicitly treated and stationarity conditions are derived. Finite-sample properties of the estimation procedure are explored by Monte Carlo simulation. An application to the Standard & Poor 500 Index indicates existence of intermediate memory (d<0) for the 1980's and no fractional differencing (d=0) but strong conditional heteroskedastic effects for the 1960's. For the latter time period, contrary to the suggestion of long memory by Mandelbrot, we only found evidence for a positive first-order autoregressive parameter.; |
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