# Tag Info

### Markowitz Eigenvalues & PCA

The main problem stems from the case opposite of the one that you are focusing on: The inversion of the covariance matrix leads to a situation where the smallest eigenvalues of the covariance matrix (...
• 1,757
Accepted

### What is the textbook answer to dealing with multicollinearity?

As one of the interviewers suggested, the expected answer starts with PCA and SVD. Before detailing it, let's take a paragraph about the way you seem to "misunderstand" the problem: ...
• 12.1k
Accepted

### PCA for portfolio optimization (Markowitz)

There is a rich body of knowledge about spectral decomposition of the covariance matrix. Gatheral and Cucuringu have very readable lectures on this topic. I also found a larger and more formal review ...
• 203

### Can PCA be used to transform a ladder of interest rate risk?

PCA is a mathematical transformation from a certain basis representation, i.e. 1y,2y,3y,4y, into another representation PC1, PC2, PC3 and PC4. In its raw form it is not a dimension reduction procedure....
• 10.2k
If $X1$ and $X2$ are almost perfectly correlated (but variance of x1 is a million times variance of x2) so that the first (normalised) pca factor is (1/sqrt(2),1/sqrt(2)) and second is (1/sqrt(2),-1/...