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Effects of outliers on the identification and estimation of GARCH models

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  • M. Angeles Carnero
  • Daniel Peña
  • Esther Ruiz

Abstract

This paper analyses how outliers affect the identification of conditional heteroscedasticity and the estimation of generalized autoregressive conditionally heteroscedastic (GARCH) models. First, we derive the asymptotic biases of the sample autocorrelations of squared observations generated by stationary processes and show that the properties of some conditional homoscedasticity tests can be distorted. Second, we obtain the asymptotic and finite sample biases of the ordinary least squares (OLS) estimator of ARCH(p) models. The finite sample results are extended to generalized least squares (GLS), maximum likelihood (ML) and quasi-maximum likelihood (QML) estimators of ARCH(p) and GARCH(1,1) models. Finally, we show that the estimated asymptotic standard deviations are biased estimates of the sample standard deviations. Copyright 2007 The Authors Journal compilation 2007 Blackwell Publishing Ltd.

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Bibliographic Info

Article provided by Wiley Blackwell in its journal Journal of Time Series Analysis.

Volume (Year): 28 (2007)
Issue (Month): 4 (07)
Pages: 471-497

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Handle: RePEc:bla:jtsera:v:28:y:2007:i:4:p:471-497

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Web page: http://www.blackwellpublishing.com/journal.asp?ref=0143-9782

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Cited by:
  1. Carmen Broto, 2012. "The effectiveness of forex interventions in four Latin American countries," Banco de Espa�a Working Papers 1226, Banco de Espa�a.
  2. Sucarrat, Genaro & Grønneberg, Steffen & Escribano, Alvaro, 2013. "Estimation and Inference in Univariate and Multivariate Log-GARCH-X Models When the Conditional Density is Unknown," MPRA Paper 49344, University Library of Munich, Germany.
  3. S. Bhaumik & M. Karanasos & A. Kartsaklas, 2008. "Derivatives Trading and the Volume-Volatility Link in the Indian Stock Market," William Davidson Institute Working Papers Series wp935, William Davidson Institute at the University of Michigan.
  4. M. Angeles Carnero & Daniel Peña & Esther Ruiz, 2008. "Estimating and Forecasting GARCH Volatility in the Presence of Outiers," Working Papers. Serie AD 2008-13, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
  5. Doornik, Jurgen A. & Ooms, Marius, 2008. "Multimodality in GARCH regression models," International Journal of Forecasting, Elsevier, vol. 24(3), pages 432-448.
  6. 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.
  7. Amélie Charles, 2008. "Forecasting volatility with outliers in GARCH models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(7), pages 551-565.
  8. Aurea Grané & Helena Veiga, 2010. "Outliers in Garch models and the estimation of risk measures," Statistics and Econometrics Working Papers ws100502, Universidad Carlos III, Departamento de Estadística y Econometría.
  9. Grané, Aurea & Veiga, Helena, 2010. "Wavelet-based detection of outliers in financial time series," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2580-2593, November.
  10. Broto, Carmen, 2011. "Inflation targeting in Latin America: Empirical analysis using GARCH models," Economic Modelling, Elsevier, vol. 28(3), pages 1424-1434, May.
  11. Dewachter, Hans & Erdemlioglu, Deniz & Gnabo, Jean-Yves & Lecourt, Christelle, 2014. "The intra-day impact of communication on euro-dollar volatility and jumps," Journal of International Money and Finance, Elsevier, vol. 43(C), pages 131-154.
  12. Alex Huang, 2011. "Volatility Modeling by Asymmetrical Quadratic Effect with Diminishing Marginal Impact," Computational Economics, Society for Computational Economics, vol. 37(3), pages 301-330, March.
  13. Karanasos, M. & Kartsaklas, A., 2009. "Dual long-memory, structural breaks and the link between turnover and the range-based volatility," Journal of Empirical Finance, Elsevier, vol. 16(5), pages 838-851, December.
  14. Charles, Amélie & Darné, Olivier, 2014. "Volatility persistence in crude oil markets," Energy Policy, Elsevier, vol. 65(C), pages 729-742.
  15. Aurea Grané & Helena Veiga, 2009. "Wavelet-based detection of outliers in volatility models," Statistics and Econometrics Working Papers ws090403, Universidad Carlos III, Departamento de Estadística y Econometría.
  16. Carnero, M. Angeles & Peña, Daniel & Ruiz, Esther, 2012. "Estimating GARCH volatility in the presence of outliers," Economics Letters, Elsevier, vol. 114(1), pages 86-90.
  17. Grossi, Luigi & Laurini, Fabrizio, 2009. "A robust forward weighted Lagrange multiplier test for conditional heteroscedasticity," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2251-2263, April.
  18. Carles Bretó & Helena Veiga, 2011. "Forecasting volatility: does continuous time do better than discrete time?," Statistics and Econometrics Working Papers ws112518, Universidad Carlos III, Departamento de Estadística y Econometría.
  19. Charles, Amélie & Darné, Olivier, 2014. "Large shocks in the volatility of the Dow Jones Industrial Average index: 1928–2013," Journal of Banking & Finance, Elsevier, vol. 43(C), pages 188-199.
  20. Aurea Grané & Belén Martín-Barragán & Helena Veiga, 2014. "Outliers in multivariate Garch models," Statistics and Econometrics Working Papers ws140503, Universidad Carlos III, Departamento de Estadística y Econometría.

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