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I am looking for help from other people with experience creating variance covariance matrix that have enough predictive power to actually lower portfolio volatility out of sample.

Using real world data I haven't been able to estimate a variance covariance matrix that has enough stability out of sample to lower portfolio volatility. My universe is the Russell 3000.

I've tried using a direct calculation of the covariance matrix from daily / weekly / monthly returns over several lookback periods ranging from 3 months to 5 years. I have also tried estimating the variance covariance matrix using a factor model with 10 - 100 factors. I've used linear regressionand other regularization methods to estimate factor betas to try to improve out of sample performance but nothing has lead to an actual decrease in out of sample volatility.

Initial research has lead me to look at Lopez de Prado's techniques from his 2016 and 2019 papers. Has anyone achieved positive out of sample results using his methods or have any other ideas about how to estimate robust / stable covariance matrix?

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