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Beta-t-(E)GARCH

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  • Harvey, A.
  • Chakravarty, T.

Abstract

The GARCH-t model is widely used to predict volatilty. However, modeling the conditional variance as a linear combination of past squared observations may not be the best approach if the standardized observations are non-Gaussian. A simple modi.cation lets the conditional variance, or its logarithm, depend on past values of the score of a t-distribution. The fact that the transformed variable has a beta distribution makes it possible to derive the properties of the resulting models. A practical consequence is that the conditional variance is more resistant to extreme observations. Extensions to deal with leverage and more than one component are discussed, as are the implications of distributions other than Student's t.

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File URL: http://www.econ.cam.ac.uk/research/repec/cam/pdf/cwpe0840.pdf
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Bibliographic Info

Paper provided by Faculty of Economics, University of Cambridge in its series Cambridge Working Papers in Economics with number 0840.

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Date of creation: Sep 2008
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Handle: RePEc:cam:camdae:0840

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Keywords: Conditional heteroskedasticity; leverage; robustness; score; Student's t; volatility.;

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  11. Shinichi Sakata & Halbert White, 1998. "High Breakdown Point Conditional Dispersion Estimation with Application to S&P 500 Daily Returns Volatility," Econometrica, Econometric Society, Econometric Society, vol. 66(3), pages 529-568, May.
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