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Spurious And Hidden Volatility

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Author Info

  • M. Angeles Carnero

    () (Universidad de Alicante)

  • Daniel Peña

    () (Universidad Carlos III de Madrid)

  • Esther Ruiz

    () (Universidad Carlos III de Madrid)

Abstract

This paper analyzes the effects caused by outliers on the identification and estimation of GARCH models. We show that outliers can lead to detect spurious conditional heteroscedasticity and can also hide genuine ARCH effects. First, we derive the asymptotic biases caused by outliers on the sample autocorrelations of squared observations and their effects on some homoscedasticity tests. Then, we obtain the asymptotic biases of the OLS estimates of ARCH(p) models and analyze their finite sample behaviour by means of extensive Monte Carlo experiments. The finite sample results are extended to GLS and ML estimates ARCH(p) and GARCH(1,1) models.

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File URL: http://www.ivie.es/downloads/docs/wpasad/wpasad-2004-45.pdf
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Bibliographic Info

Paper provided by Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie) in its series Working Papers. Serie AD with number 2004-45.

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Length: 44 pages
Date of creation: Nov 2004
Date of revision:
Publication status: Published by Ivie
Handle: RePEc:ivi:wpasad:2004-45

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Keywords: GARCH; Outliers; Heteroscedasticity;

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References

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  1. Shiqing Ling & Michael McAleer, 2001. "Asymptotic Theory for a Vector ARMA-GARCH Model," ISER Discussion Paper 0549, Institute of Social and Economic Research, Osaka University.
  2. Nathan S. Balke & Thomas B. Fomby, 1991. "Large shocks, small shocks, and economic fluctuations: outliers in macroeconomic times series," Research Paper 9101, Federal Reserve Bank of Dallas.
  3. Philip Hans Franses & Dick van Dijk & André Lucas, 1998. "Short Patches of Outliers, ARCH and Volatility Modeling," Tinbergen Institute Discussion Papers 98-057/4, Tinbergen Institute.
  4. Arup Bose & Kanchan Mukherjee, 2003. "Estimating The Arch Parameters By Solving Linear Equations," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(2), pages 127-136, 03.
  5. Xibin Zhang, 2004. "Assessment of Local Influence in GARCH Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(2), pages 301-313, 03.
  6. Weiss, Andrew A., 1986. "Asymptotic Theory for ARCH Models: Estimation and Testing," Econometric Theory, Cambridge University Press, vol. 2(01), pages 107-131, April.
  7. Aggarwal, Reena & Inclan, Carla & Leal, Ricardo, 1999. "Volatility in Emerging Stock Markets," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 34(01), pages 33-55, March.
  8. Pena D. & Rodriguez J., 2002. "A Powerful Portmanteau Test of Lack of Fit for Time Series," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 601-610, June.
  9. Franses, Philip Hans & Ghijsels, Hendrik, 1999. "Additive outliers, GARCH and forecasting volatility," International Journal of Forecasting, Elsevier, vol. 15(1), pages 1-9, February.
  10. Benjamin M. Friedman & David I. Laibson, 1989. "Economic Implications of Extraordinary Movements in Stock Prices," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 20(2), pages 137-190.
  11. Allan W. Gregory & Jonathan J. Reeves, 2010. "Estimation and Inference in ARCH Models in the Presence of Outliers," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 8(4), pages 547-549, Fall.
  12. Wooldridge, Jeffrey M., 1990. "A Unified Approach to Robust, Regression-Based Specification Tests," Econometric Theory, Cambridge University Press, vol. 6(01), pages 17-43, March.
  13. Dijk, D.J.C. van & Franses, Ph.H.B.F. & Lucas, A., 1996. "Testing for ARCH in the Presence of Additive Outliers," Econometric Institute Report EI 9659-/A, Erasmus University Rotterdam, Econometric Institute.
  14. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
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Citations

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Cited by:
  1. Beum-Jo Park, 2009. "Risk-return relationship in equity markets: using a robust GMM estimator for GARCH-M models," Quantitative Finance, Taylor and Francis Journals, vol. 9(1), pages 93-104.
  2. Rodríguez, Julio & Ruiz, Esther, 2005. "A powerful test for conditional heteroscedasticity for financial time series with highly persistent volatilities.," Open Access publications from Universidad Carlos III de Madrid info:hdl:10016/4910, Universidad Carlos III de Madrid.
  3. Pascual, Lorenzo & Romo, Juan & Ruiz, Esther, 2006. "Bootstrap prediction for returns and volatilities in GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2293-2312, May.

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