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Robust Measurement of Beta Risk

Published online by Cambridge University Press:  06 April 2009

Louis K. C. Chan  and
Josef Lakonishok
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Abstract

Many empirical studies find that the distribution of stock returns departs from normality. In such cases, it is desirable to employ a statistical estimation procedure that may be more efficient than ordinary least squares. This paper describes various robust methods, which have attracted increasing attention in the statistical literature, in the context of estimating beta risk. The empirical analysis documents the potential efficiency gains from using robust methods as an alternative to ordinary least squares, based on both simulated and actual returns data.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 27 , Issue 2 , June 1992 , pp. 265 - 282
Copyright
Copyright © School of Business Administration, University of Washington 1992

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