Overview - 2024.2 English - XD160

Vitis Libraries

Document ID
XD160
Release Date
2024-11-29
Version
2024.2 English

Principal Component Analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of linearly uncorrelated variables called Principal Components.

In quantitative finance, PCA can be directly applied to risk management of interest rate derivative portfolios. It helps reduce the complexity of swap tradings from a function of 30-500 market instruments to, usually, just 3 or 4, which can represent the interest rate paths on a macro basis.