Abstract
The estimation of β plays a basic role in the evaluation of expected return and market risk. Typically this is performed by ordinary least squares (OLS). To cope with the high sensitivity of OLS to outlying observations and to deviations from the normality assumptions, several methods suggest to use robust estimators. It is argued that, from a predictive point of view, the simple use of either OLS or robust estimators is not sufficient but that some shrinking of the robust estimators toward OLS is necessary to reduce the mean squared error. The performance of the proposed shrinkage robust estimator is shown by means of a small simulation study and on a real data set.
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Genton, M.G., Ronchetti, E. (2008). Robust Prediction of Beta. In: Kontoghiorghes, E.J., Rustem, B., Winker, P. (eds) Computational Methods in Financial Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77958-2_8
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DOI: https://doi.org/10.1007/978-3-540-77958-2_8
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