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


  • Ma Jun

    () (University of Washington)

  • Nelson Charles R

    () (University of Washington)

  • Startz Richard

    () (University of Washington)


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.

Suggested Citation

  • Ma Jun & Nelson Charles R & Startz Richard, 2007. "Spurious Inference in the GARCH (1,1) Model When It Is Weakly Identified," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 11(1), pages 1-27, March.
  • Handle: RePEc:bpj:sndecm:v:11:y:2007:i:1:n:1

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    References listed on IDEAS

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    Cited by:

    1. Cristhian Mellado & Diego Escobari, 2015. "Virtual integration of financial markets: a dynamic correlation analysis of the creation of the Latin American Integrated Market," Applied Economics, Taylor & Francis Journals, vol. 47(19), pages 1956-1971, April.
    2. Liu, Yan & Luger, Richard, 2009. "Efficient estimation of copula-GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2284-2297, April.
    3. Enders, Walter & Ma, Jun, 2011. "Sources of the great moderation: A time-series analysis of GDP subsectors," Journal of Economic Dynamics and Control, Elsevier, vol. 35(1), pages 67-79, January.
    4. Donald W. K. Andrews & Patrik Guggenberger, 2014. "A Conditional-Heteroskedasticity-Robust Confidence Interval for the Autoregressive Parameter," The Review of Economics and Statistics, MIT Press, vol. 96(2), pages 376-381, May.
    5. Trypsteen, Steven, 2017. "The growth-volatility nexus: New evidence from an augmented GARCH-M model," Economic Modelling, Elsevier, vol. 63(C), pages 15-25.
    6. Kishor, N. Kundan & Marfatia, Hardik A., 2013. "The time-varying response of foreign stock markets to U.S. monetary policy surprises: Evidence from the Federal funds futures market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 24(C), pages 1-24.
    7. Sarkar, Asani & Zhang, Lingjia, 2009. "Time varying consumption covariance and dynamics of the equity premium: Evidence from the G7 countries," Journal of Empirical Finance, Elsevier, vol. 16(4), pages 613-631, September.
    8. Luger, Richard, 2012. "Finite-sample bootstrap inference in GARCH models with heavy-tailed innovations," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3198-3211.
    9. Kishor, N. Kundan & Kumari, Swati & Song, Suyong, 2015. "Time variation in the relative importance of permanent and transitory components in the U.S. housing market," Finance Research Letters, Elsevier, vol. 12(C), pages 92-99.

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