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Temporal aggregation of GARCH processes

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  • Drost, F.C.

    (Tilburg University, School of Economics and Management)

  • Nijman, T.E.

    (Tilburg University, School of Economics and Management)

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., 1994. "Temporal aggregation of GARCH processes," Other publications TiSEM b6718003-2fa5-43bb-a690-d, Tilburg University, School of Economics and Management.
  • Handle: RePEc:tiu:tiutis:b6718003-2fa5-43bb-a690-d1fba39e78d8
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    References listed on IDEAS

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    2. Gallant, Ronald & Tauchen, George, 1989. "Seminonparametric Estimation of Conditionally Constrained Heterogeneous Processes: Asset Pricing Applications," Econometrica, Econometric Society, vol. 57(5), pages 1091-1120, September.
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    9. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
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