Principal Component Analysis for the Nonlinear Portfolio Model

Abstract

The present study improves the nonlinear portfolio model by using principal component analysis. To enhance the portfolio effect of spreading risks efficiently, we aim for lower correlations among each asset movement. For this reason, we apply the principal components of assets to the nonlinear portfolio model, which uses nonlinear prediction to estimate future movements. However, because we are not sure whether these principal components have nonlinearity, we perform Fourier-shuffled surrogate tests on the principal components. Finally, we confirm the efficiency of our nonlinear principal-component portfolio model through some investment simulations with real financial data.