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Spurious Inference in the GARCH (1,1) Model When It Is Weakly Identified
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March 1, 2007
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.
Published Online: 2007-3-1
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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- Spurious Inference in the GARCH (1,1) Model When It Is Weakly Identified
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- Time Series Models for Forecasting: Testing or Combining?
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Articles in the same Issue
- Article
- Spurious Inference in the GARCH (1,1) Model When It Is Weakly Identified
- Gains from Synchronization
- Time Series Models for Forecasting: Testing or Combining?
- Short-Run Patience and Wealth Inequality
- A Smooth Transition Autoregressive Conditional Duration Model
- Fractionally Integrated Long Horizon Regressions
- A New Application of Exact Nonparametric Methods to Long-Horizon Predictability Tests
