Iterated and exponentially weighted moving principal component analysis

Paul Bilokon and 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.

Date: 2021-08
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cwa, nep-ecm, nep-ets and nep-isf
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