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Estimating and Forecasting GARCH Volatility in the Presence of Outiers

Author

Listed:
  • M. Angeles Carnero

    (Universidad de Alicante)

  • Daniel Peña

    (Universidad Carlos III de Madrid)

  • Esther Ruiz

    (Universidad Carlos III de Madrid)

Abstract

The main goal when fitting GARCH models to conditionally heteroscedastic time series is to estimate the underlying volatilities. It is well known that outliers affect the estimation of the GARCH parameters. However, little is known about their effects when estimating volatilities. In this paper, we show that when estimating the volatility by using Maximum Likelihood estimates of the parameters, the biases incurred can be very large even if estimated parameters have small biases. Consequently, we propose to use robust procedures. In particular, a simple robust estimator of the parameters is proposed and shown that its properties are comparable with other more complicated ones available in the literature. The properties of the estimated and predicted volatilities obtained by using robust filters based on robust parameter estimates are analyzed. All the results are illustrated using daily S&P500 and IBEX35 returns.

Suggested Citation

  • 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).
    • Handle: RePEc:ivi:wpasad:2008-13
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    References listed on IDEAS

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    4. van Dijk, Dick & Franses, Philip Hans & Lucas, Andre, 1999. "Testing for ARCH in the Presence of Additive Outliers," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(5), pages 539-562, Sept.-Oct.
    5. M. Angeles Carnero & Daniel Peña & Esther Ruiz, 2007. "Effects of outliers on the identification and estimation of GARCH models," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(4), pages 471-497, July.
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    Cited by:

    1. Laurent, Sébastien & Lecourt, Christelle & Palm, Franz C., 2016. "Testing for jumps in conditionally Gaussian ARMA–GARCH models, a robust approach," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 383-400.
    2. 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.
    3. María José Rodríguez & Esther Ruiz, 2012. "Revisiting Several Popular GARCH Models with Leverage Effect: Differences and Similarities," Journal of Financial Econometrics, Oxford University Press, vol. 10(4), pages 637-668, September.

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    More about this item

    Keywords

    Heteroscedasticity; M-estimator; QML estimator; Robustness; Financial Markets;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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