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Inference in (M)GARCH Models in the Presence of Additive Outliers: Specification, Estimation, and Prediction

In: Advances in Mathematics and Applications

Author

Listed:
  • Luiz Koodi Hotta

    (University of Campinas, Institute of Mathematics, Statistics and Scientific Computing)

  • Carlos Trucíos

    (Getúlio Vargas Foundation, São Paulo School of Economics)

Abstract

The (M)GARCH models are probably the most widely used to estimate and predict volatility. Estimation and prediction of volatility are very important in many financial applications. One important issue in the application of (M)GARCH models is the frequent presence of outliers in financial time series and their effects in all stages of model application. We present some issues involved in making inference in (M)GARCH models in the presence of additive outliers. Specifically, we present the effects of outliers on specification, estimation of models, and their volatility and volatility prediction. We also present some robust methods to estimate the model and to predict volatility. We emphasize the presentation of robust methods for volatility forecast density.

Suggested Citation

  • Luiz Koodi Hotta & Carlos Trucíos, 2018. "Inference in (M)GARCH Models in the Presence of Additive Outliers: Specification, Estimation, and Prediction," Springer Books, in: Carlile Lavor & Francisco A. M. Gomes (ed.), Advances in Mathematics and Applications, pages 179-202, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-94015-1_8
    DOI: 10.1007/978-3-319-94015-1_8
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