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

Luiz Koodi Hotta () and Carlos Trucíos
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Luiz Koodi Hotta: University of Campinas, Institute of Mathematics, Statistics and Scientific Computing
Carlos Trucíos: Getúlio Vargas Foundation, São Paulo School of Economics

A chapter in Advances in Mathematics and Applications, 2018, pp 179-202 from Springer

Abstract: 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.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-94015-1_8

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DOI: 10.1007/978-3-319-94015-1_8

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