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Temporal Aggregation of Garch Processes

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  • Drost, F.C.
  • Nijman, T.E.

Abstract

The authors derive low frequency, say weekly, models implied by high frequency, say daily, ARMA models with symmetric GARCH errors. They show that low frequency models exhibit conditional heteroskedasticity of the GARCH form as well. The parameters in the conditional variance equation of the low frequency model depend upon mean, variance, and kurtosis parameters of the corresponding high frequency model. Moreover, strongly consistent estimators of the parameters in the high frequency model can be derived from low frequency data. The common assumption in applications that rescaled innovations are independent is disputable, since it depends upon the available data frequency. Copyright 1993 by The Econometric Society.
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Suggested Citation

  • Drost, F.C. & Nijman, T.E., 1992. "Temporal Aggregation of Garch Processes," Papers 9240, Tilburg - Center for Economic Research.
    • Handle: RePEc:fth:tilbur:9240
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    1. Nelson, Daniel B., 1990. "ARCH models as diffusion approximations," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 7-38.
    2. Eijffinger, Sylvester & van Rixtel, Adrian, 1992. "The Japanese financial system and monetary policy: a descriptive review," Japan and the World Economy, Elsevier, vol. 4(4), pages 291-309, December.
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    13. Nijman, T.E. & Verbeek, M.J.C.M., 1992. "Testing for selectivity in panel data models," Other publications TiSEM 7ec34a6c-1d84-4052-971c-d, Tilburg University, School of Economics and Management.
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    economic models ; econometrics;

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