Ledoit-Wolf Shrinkage
A method that smooths a noisy covariance matrix toward a structured target, producing more reliable optimizer inputs.
Category: Methodology
What is Ledoit-Wolf Shrinkage?
Sample covariance matrices are noisy when the number of assets is large relative to history. Ledoit-Wolf shrinks the sample matrix toward a constant-correlation or identity target with a data-driven blend coefficient.