Iterated and exponentially weighted moving principal component analysis
2021-08-30SSRN 2021Code Available1· sign in to hype
Paul Bilokon, David Finkelstein
Code Available — Be the first to reproduce this paper.
ReproduceCode
- github.com/sydx/xpcaOfficialIn papernone★ 15
Abstract
The principal component analysis (PCA) is a staple statistical and unsupervised machine learning technique in finance. The application of PCA in a financial setting is associated with several technical difficulties, such as numerical instability and nonstationarity. We attempt to resolve them by proposing two new variants of PCA: an iterated principal component analysis (IPCA) and an exponentially weighted moving principal component analysis (EWMPCA). Both variants rely on the Ogita-Aishima iteration as a crucial step.