Tuesday, April 5th @ 11:00-12:30 PM
Bias reduction in optimized portfolios through Multiple Anchor Point Shrinkage (MAPS)
Hubeyb Gurdogan, CDAR
Abstract: Estimation error in a covariance matrix distorts optimized portfolios, and the effect is pronounced when the number of securities p exceeds the number of observations n. In the HL regime where p >> n, we show that a material component of the distortion can be attributed to optimization biases that correspond to the constraints used to construct the portfolio. Using Multiple Anchor Point Shrinkage (MAPS) for eigenvectors developed in Gurdogan & Kercheval (2021), we materially eliminate these optimization biases for large p, and zero them out asymptotically, leading to more accurate portfolios. This work extends the correction of the dispersion bias in Goldberg, Papanicolaou & Shkolnik (2022).