2021/08/30 by Paul Bilokon, David Finkelstein, Bilokon, Paul +1
Economics, Econometrics and Finance · #FOS: Economics and business #Statistical Finance (q-fin.ST) #q-fin.ST
paper · pdf · doi:10.48550/arxiv.2108.13072
9 pages, 5 figures
arxiv created 2021/08/30 · arxiv updated 2021/08/31
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.