  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.
