Systematic risk estimation in the presence of large and many outliers

  • S. G. Badrinath
  • Sangit Chatterjee


It is well recognized that the effect of extreme points on systematic risk estimates is not adequately captured through least squares estimation. This article uses the reweighted least median squares (RWLMS) approach, first proposed by Rousseeuw (1984), which accurately detects outlier presence. Using a large sample of 1350 NYSE/AMEX firms, the article demonstrates that least squares does indeed mask several potentially influential points, that this masking is very pervasive over the sample, and that it may persist even after conventional robust estimation techniques are applied. When these masked points are “unmasked” by RWLMS and zero weights assigned to such observations, the resulting RWLMS estimates of beta are on average 10%–15% smaller. However, a Bayesian treatment of such points (assigning a priori nonzero weights) is possible in both one and two factor market models.

Key words

systematic risk estimation reweighted least median squares outliers masking 


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Copyright information

© Kluwer Academic Publishers 1993

Authors and Affiliations

  • S. G. Badrinath
    • 1
  • Sangit Chatterjee
    • 1
  1. 1.Management Science DepartmentNortheastern UniversityBoston

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