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arXiv · 2108.13072

Iterated and exponentially weighted moving principal component analysis

Paul Bilokon·David Finkelstein
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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.

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Paul Bilokon, David Finkelstein. 2021-08-30. Iterated and exponentially weighted moving principal component analysis. https://arxiv.org/abs/2108.13072

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