Covariance matrix shrinkage: Ledoit and Wolf (2004) 

Sample covariance matrix applications in portfolio optimisation are often criticised for the excessive noise that such matrices contain which results in "estimation error maximisation" and unrealistic optimal portfolios. Covariance matrix shrinkage as proposed by Ledoit and Wolf (2004) is one of the most commonly used and of the most elegant remedies for this issue. Today we are investigating how to shrink a real-world asset return covariance matrix, discuss the concepts behind the procedure, and how an application of shrunk versus non-shrunk matrix impacts portfolio management.
Don't forget to subscribe to NEDL and give this video a thumbs up for more videos in Investment Managemet!
Please consider supporting NEDL on Patreon: / nedleducation