Systematic risk estimation in the presence of large and many outliers |
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Authors: | S G Badrinath Sangit Chatterjee |
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Institution: | (1) Management Science Department, Northeastern University, 219 Hayden Hall, 02115 Boston, MA |
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Abstract: | 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. |
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Keywords: | systematic risk estimation reweighted least median squares outliers masking |
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