We study a regression-based estimation of the tracking error frontier under the normality assumption. In terms of the sample efficient set constants, we document a lower tracking error variance than the traditional plug-in estimator. Furthermore, our approach provides significant computation benefits for tracing out the tracking error frontier in the mean-variance plane.

tracking error frontier, mean-variance analysis, efficient constants, tangency portfolio, minimum-variance portfolio.