Overview - 2023.2 English

Vitis Libraries

Release Date
2023-12-20
Version
2023.2 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 reducing 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.