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Spurious Inference in the GARCH(1,1) Model When It Is Weakly Identified

  • Jun Ma
  • Charles Nelson
  • Richard Startz

This paper shows that the Zero-Information-Limit-Condition (ZILC) formulated by Nelson and Startz (2006) holds in the GARCH(1,1) model. As a result, the GARCH estimate tends to have too small a standard error relative to the true one when the ARCH parameter is small, even when sample size becomes very large. In combination with an upward bias in the GARCH estimate, the small standard error will often lead to the spurious inference that volatility is highly persistent when it is not. We develop an empirical strategy to deal with this issue and show how it applies to real datasets.

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Paper provided by University of Washington, Department of Economics in its series Working Papers with number UWEC-2006-14-P.

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Date of creation: Mar 2007
Date of revision: Mar 2007
Publication status: Published in Studies in Nonlinear Dynamics & Econometrics, Volume Vol.11, Article 1
Handle: RePEc:udb:wpaper:uwec-2006-14-p
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