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Effects of Level Outliers on the Identification and Estimation of GARCH Models

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  • E. Ruiz
  • M.A. Carnero
  • D. Pereira

Abstract

In this paper, we study the effects caused by the presence of outliers on the identification and estimation of GARCH models. First, we derive the asymptotic biases of the sample autocorrelations of squared observations and their effects on some popular homoscedasticity tests when uncorrelated GARCH series are contaminated by level outliers. Then, we obtain the asymptotic biases of the OLS estimates of the parameters of ARCH(p) models and analyze their finite sample behavior by means of extensive Monte Carlo experiments. The finite sample results are also extended to ML estimates of ARCH(p) and GARCH(1,1) models. The results are illustrated analyzing real series of financial ret

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Bibliographic Info

Paper provided by Econometric Society in its series Econometric Society 2004 Australasian Meetings with number 21.

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Date of creation: 11 Aug 2004
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Handle: RePEc:ecm:ausm04:21

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Keywords: Autocorrelations; Heteroscedasticity testing; Maximum Likelihood; Ordinary Least Squares;

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  1. Bollerslev, Tim, 1987. "A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return," The Review of Economics and Statistics, MIT Press, vol. 69(3), pages 542-47, August.
  2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
  3. Bollerslev, Tim & Engle, Robert F. & Nelson, Daniel B., 1986. "Arch models," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 49, pages 2959-3038 Elsevier.
  4. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
  5. 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.
  6. van Dijk, D.J.C. & Franses, Ph.H.B.F. & Lucas, A., 1996. "Testing for ARCH in the Presence of Additive Outliers," Econometric Institute Research Papers EI 9659-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  7. Shinichi Sakata & Halbert White, 1998. "High Breakdown Point Conditional Dispersion Estimation with Application to S&P 500 Daily Returns Volatility," Econometrica, Econometric Society, vol. 66(3), pages 529-568, May.
  8. 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.
  9. Jurgen Doornik & Marius Ooms, 2005. "Outlier Detection in GARCH Models," Economics Series Working Papers 2005-W24, University of Oxford, Department of Economics.
  10. Robin L. Lumsdaine & Serena Ng, 1998. "Testing for ARCH in the Presence of a Possibly Misspecified Conditional Mean," Boston College Working Papers in Economics 370, Boston College Department of Economics.
  11. Franses, Philip Hans & Ghijsels, Hendrik, 1999. "Additive outliers, GARCH and forecasting volatility," International Journal of Forecasting, Elsevier, vol. 15(1), pages 1-9, February.
  12. Tim Bollerslev & Jeffrey M. Wooldridge, 1988. "Quasi-Maximum Likelihood Estimation of Dynamic Models with Time-Varying Covariances," Working papers 505, Massachusetts Institute of Technology (MIT), Department of Economics.
  13. Lee, Sang-Won & Hansen, Bruce E., 1994. "Asymptotic Theory for the Garch(1,1) Quasi-Maximum Likelihood Estimator," Econometric Theory, Cambridge University Press, vol. 10(01), pages 29-52, March.
  14. Lee, John H. H., 1991. "A Lagrange multiplier test for GARCH models," Economics Letters, Elsevier, vol. 37(3), pages 265-271, November.
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Cited by:
  1. Ruiz, Esther & Veiga, Helena, 2008. "Modelling long-memory volatilities with leverage effect: A-LMSV versus FIEGARCH," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 2846-2862, February.

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