11 votes

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 (...
Hans-Peter Schrei's user avatar
7 votes
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: ...
lehalle's user avatar
  • 12.1k
3 votes
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 ...
Adam N.'s user avatar
  • 203
3 votes

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....
Attack68's user avatar
  • 10.2k
2 votes

PCA factors not uncorrelated

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/...
Arshdeep's user avatar
  • 2,045
2 votes

PCA analysis within Private Credit

The PCA approach on listed equities is based on the time series of returns of all the considered stocks. You generally do not have such time series for private equities (since they do not have a ...
lehalle's user avatar
  • 12.1k

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