# Compare portfolio variance using different regularizers

I'm given a question like below. Using the 48_Industry_Portfolios_daily dataset: characterize/describe the dataset and focus on the global minimum variance portfolio. Compare the portfolio variance using different regularizers and use validation methods to find the optimal parameters.

What I'm not clear is to compare the portfolio variance with different regularizers and to use validation methods.

I was using Python to find the efficient frontier. What I need to know is, are there any useful python materials where I can compare portfolio variance using different regularizes. I was not able to find useful resources

• Hi please find the attached link. This explains how I used it. I have shown only 9 parameters. But there are 48 – Hiru Feb 28 '19 at 7:22

When you solve for a minimum variance portfolio you acquire some values, $$\mathbf{\beta}$$ corresponding to the weights of your assets, usually such that $$\sum \mathbf{\beta} = 1$$.
Regularization means you try to limit these values such that your objective function also includes the norm of $$\mathbf{\beta}$$ (Ridge regression - L2-norm) or the sum of absolute values of $$\mathbf{\beta}$$ (Lasso - L1-norm). If you allow short selling this means you will try to avoid the case where one asset might have a weight of -1000 and another +1000.