Practice with PyPortfolioOpt: covariance

Portfolio optimization relies upon an unbiased and efficient estimate of asset covariance. Although sample covariance is unbiased, it is not efficient--extreme events tend to be overweighted.

One approach to alleviate this is through "covariance shrinkage", where large errors are reduced ('shrunk') to improve efficiency. In this exercise, you'll use pypfopt.risk_models's CovarianceShrinkage object to transform sample covariance into an efficient estimate. The textbook error shrinkage method, .ledoit_wolf(), is a method of this object.

Asset prices are available in your workspace. Note that although the CovarianceShrinkage object takes prices as input, it actually calculates the covariance matrix of asset returns, not prices.

Import the CovarianceShrinkage object from the pypfopt.risk_models module.
Create the CovarianceShrinkage instance variable cs, the covariance matrix of returns.

Exercise

Practice with PyPortfolioOpt: covariance

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