Robust Regression Estimation Methods and Intercept Bias: A Capital Asset Pricing Model Application

Robust estimation techniques based on symmetric probability distributions are often substituted for OLS to obtain efficient regression parameters with thick-tail distributed data. The empirical, simulation and theoretical results in this paper show that with skewed distributed data, symmetric robust estimation techniques produce biased regression intercepts. This paper evaluates robust methods in estimating the capital asset pricing model and shows skewed stock returns data used with symmetric robust estimation techniques produce biased alphas. The results support the recommendation that robust estimation using the skewed generalized T family of distributions may be used to obtain more efficient and unbiased estimates with skewness.