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

  • Luiz Koodi HottaEmail author
  • Carlos Trucíos
Chapter

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.

Notes

Acknowledgements

The first author acknowledges financial support from São Paulo Research Foundation (FAPESP), grants 2013/00506-1 and 2013/22930-0. The second author is also grateful for financial support from FAPESP, grants 2012/09596-0 and 2016/18599-4. Both authors acknowledge the support of the Centre of Applied Research on Econometrics, Finance and Statistics (CAREFS).

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Authors and Affiliations

  1. 1.Institute of Mathematics, Statistics and Scientific ComputingUniversity of CampinasCampinasBrazil
  2. 2.São Paulo School of EconomicsGetúlio Vargas FoundationSão PauloBrazil

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