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Outlier Detection in GARCH Models




We present a new procedure for detecting multiple additive outliers in GARCH(1,1) models at unknown dates. The outlier candidates are the observations with the largest standardized residual. First, a likelihood-ratio based test determines the presence and timing of an outlier. Next, a second test determines the type of additive outlier (volatility or level). The tests are shown to be similar with respect to the GARCH parameters. Their null distribution can be easily approximated from an extreme value distribution, so that computation of p-values does not require simulation. The procedure outperforms alternative methods, especially when it comes to determining the date of the outlier. We apply the method to returns of the Dow Jones index, using monthly, weekly, and daily data. The procedure is extended and applied to GARCH models with Student-t distributed errors.

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  • Jurgen A. Doornik & Marius Ooms, 2005. "Outlier Detection in GARCH Models," Economics Papers 2005-W24, Economics Group, Nuffield College, University of Oxford.
  • Handle: RePEc:nuf:econwp:0524

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    References listed on IDEAS

    1. 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.
    2. Jurgen A. Doornik & Marius Ooms, 2003. "Multimodality in the GARCH Regression Model," Economics Papers 2003-W20, Economics Group, Nuffield College, University of Oxford.
    3. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    4. Neil Shephard, 2005. "Stochastic Volatility," Economics Papers 2005-W17, Economics Group, Nuffield College, University of Oxford.
    5. 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 542-547, August.
    6. 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.
    7. 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.
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    Cited by:

    1. E. Ruiz & M.A. Carnero & D. Pereira, 2004. "Effects of Level Outliers on the Identification and Estimation of GARCH Models," Econometric Society 2004 Australasian Meetings 21, Econometric Society.
    2. Charles, Amélie & Darné, Olivier, 2014. "Volatility persistence in crude oil markets," Energy Policy, Elsevier, vol. 65(C), pages 729-742.
    3. 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.
    4. Jurgen A. Doornik & Marius Ooms, 2003. "Multimodality in the GARCH Regression Model," Economics Papers 2003-W20, Economics Group, Nuffield College, University of Oxford.
    5. Eugenia Sanin, María & Violante, Francesco & Mansanet-Bataller, María, 2015. "Understanding volatility dynamics in the EU-ETS market," Energy Policy, Elsevier, vol. 82(C), pages 321-331.
    6. Amélie Charles, 2008. "Forecasting volatility with outliers in GARCH models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(7), pages 551-565.
    7. Fagiani, Riccardo & Hakvoort, Rudi, 2014. "The role of regulatory uncertainty in certificate markets: A case study of the Swedish/Norwegian market," Energy Policy, Elsevier, vol. 65(C), pages 608-618.
    8. Maurício Yoshinori Une & Marcelo Savino Portugal, 2005. "Can fear beat hope? A story of GARCH-in-Mean-Level effects for Emerging Market Country Risks," Econometrics 0509006, EconWPA.
    9. Grané, Aurea & Veiga, Helena, 2010. "Outliers in Garch models and the estimation of risk measures," DES - Working Papers. Statistics and Econometrics. WS ws100502, Universidad Carlos III de Madrid. Departamento de Estadística.
    10. Mangold, Benedikt & Pleier, Thomas & Brug, Christoph & Nolzen, Jan & Stübinger, Johannes, 2014. "Verbesserung des Lernverhaltens durch Online-Tests: Ein Jahr später," Discussion Papers 91/2013, Friedrich-Alexander University Erlangen-Nuremberg, Chair of Statistics and Econometrics.
    11. Kocenda, Evzen & Valachy, Juraj, 2006. "Exchange rate volatility and regime change: A Visegrad comparison," Journal of Comparative Economics, Elsevier, vol. 34(4), pages 727-753, December.
    12. Koenig, P., 2011. "Modelling Correlation in Carbon and Energy Markets," Cambridge Working Papers in Economics 1123, Faculty of Economics, University of Cambridge.
    13. Beum-Jo Park, 2009. "Risk-return relationship in equity markets: using a robust GMM estimator for GARCH-M models," Quantitative Finance, Taylor & Francis Journals, vol. 9(1), pages 93-104.
    14. Behmiri, Niaz Bashiri & Manera, Matteo, 2015. "The role of outliers and oil price shocks on volatility of metal prices," Resources Policy, Elsevier, vol. 46(P2), pages 139-150.
    15. repec:eee:finana:v:52:y:2017:i:c:p:292-308 is not listed on IDEAS
    16. 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).
    17. Doornik, Jurgen A. & Ooms, Marius, 2008. "Multimodality in GARCH regression models," International Journal of Forecasting, Elsevier, vol. 24(3), pages 432-448.
    18. Puigvert Gutiérrez, Josep Maria & Fortiana Gregori, Josep, 2008. "Clustering techniques applied to outlier detection of financial market series using a moving window filtering algorithm," Working Paper Series 948, European Central Bank.
    19. Zhang, Dayong & Dickinson, David & Barassi, Marco, 2008. "Volatility Switching in Shanghai Stock Exchange: Does regulation help reduce volatility?," MPRA Paper 70352, University Library of Munich, Germany.
    20. Grané, Aurea & Veiga, Helena, 2009. "Wavelet-based detection of outliers in volatility models," DES - Working Papers. Statistics and Econometrics. WS ws090403, Universidad Carlos III de Madrid. Departamento de Estadística.
    21. Mora Galán, Alberto & Pérez, Ana & Ruiz, Esther, 2004. "Stochastic volatility models and the Taylor effect," DES - Working Papers. Statistics and Econometrics. WS ws046315, Universidad Carlos III de Madrid. Departamento de Estadística.
    22. 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.

    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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