IDEAS home Printed from https://ideas.repec.org/p/par/dipeco/2006-se02.html
   My bibliography  Save this paper

Robust volatility forecasts and model selection in financial time series

Author

Listed:
  • L. Grossi
  • G. Morelli

    ()

Abstract

In order to cope with the stylized facts of financial time series, many models have been proposed inside the GARCH family (e.g. EGARCH, GJR-GARCH, QGARCH, FIGARCH, LSTGARCH) and the stochastic volatility models (e.g. SV). Generally, all these models tend to produce very similar results as concerns forecasting performance. Most of the time it is difficult to choose which is the most appropriate specification. In addition, all these models are very sensitive to the presence of atypical observations. The purpose of this paper is to provide the user with new robust model selection procedures in financial models which downweight or eliminate the effect of atypical observations. The extreme case is when outliers are treated as missing data. In this paper we extend the theory of missing data to the family of GARCH models and show how to robustify the loglikelihood to make it insensitive to the presence of outliers. The suggested procedure enables us both to detect atypical observations and to select the best models in terms of forecasting performance.

Suggested Citation

  • L. Grossi & G. Morelli, 2006. "Robust volatility forecasts and model selection in financial time series," Economics Department Working Papers 2006-SE02, Department of Economics, Parma University (Italy).
  • Handle: RePEc:par:dipeco:2006-se02
    as

    Download full text from publisher

    File URL: http://swrwebeco.econ.unipr.it/RePEc/pdf/VII_2006-02.pdf
    Download Restriction: no

    References listed on IDEAS

    as
    1. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    2. Riani Marco, 2004. "Extensions of the Forward Search to Time Series," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 8(2), pages 1-25, May.
    3. Xibin Zhang, 2004. "Assessment of Local Influence in GARCH Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(2), pages 301-313, March.
    4. González-Rivera Gloria, 1998. "Smooth-Transition GARCH Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 3(2), pages 1-20, July.
    5. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. " On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    6. Enrique Sentana, 1995. "Quadratic ARCH Models," Review of Economic Studies, Oxford University Press, vol. 62(4), pages 639-661.
    7. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    8. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
    9. Park, Beum-Jo, 2002. "An Outlier Robust GARCH Model and Forecasting Volatility of Exchange Rate Returns," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(5), pages 381-393, August.
    10. Rossi, Alessandro & Gallo, Giampiero M., 2006. "Volatility estimation via hidden Markov models," Journal of Empirical Finance, Elsevier, vol. 13(2), pages 203-230, March.
    11. Engle, Robert F & Lilien, David M & Robins, Russell P, 1987. "Estimating Time Varying Risk Premia in the Term Structure: The Arch-M Model," Econometrica, Econometric Society, vol. 55(2), pages 391-407, March.
    12. Charles, Amelie & Darne, Olivier, 2005. "Outliers and GARCH models in financial data," Economics Letters, Elsevier, vol. 86(3), pages 347-352, March.
    13. Grossi Luigi, 2004. "Analyzing Financial Time Series through Robust Estimators," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 8(2), pages 1-15, May.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    GARCH models; extreme value; robust estimation;

    JEL classification:

    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:par:dipeco:2006-se02. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Andrea Lasagni) or (Rebekah McClure). General contact details of provider: http://edirc.repec.org/data/feparit.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.