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.
This exercise is part of the course
Quantitative Risk Management in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Import the CovarianceShrinkage object
from pypfopt.risk_models import ____
# Create the CovarianceShrinkage instance variable
cs = ____(prices)