Portfolio Efficiency with High-Dimensional Data As Conditioning Information

77 Pages Posted: 29 Sep 2020

See all articles by Caio Vigo Pereira

Caio Vigo Pereira

University of Kansas - Department of Economics

Date Written: September 14, 2020

Abstract

In this paper, we build efficient portfolios using different frameworks proposed in the literature with several datasets containing an increasing number of predictors as conditioning information. We carry an extensive empirical study to investigate several approaches to impose sparsity and dimensionality reduction, as well as possible latent factors driving the returns of the risky assets. In contrast to previous studies that made use of naive OLS and low-dimension information sets, we find that (i) accounting for large conditioning information sets, and (ii) the use of variable selection, shrinkage methods and factors models, such as the principal component regression and the partial least squares provide better out-of-sample results as measured by Sharpe ratios.

Keywords: Dimensionality reduction, Shrinkage, Efficient Portfolios, Principal Components Regression (PCR), Partial Least Squares (PLS), Three-Pass Regression Filter (3PRF), Ridge Regression, LASSO

JEL Classification: G11, G17, C32, C38

Suggested Citation

Vigo Pereira, Caio, Portfolio Efficiency with High-Dimensional Data As Conditioning Information (September 14, 2020). Available at SSRN: https://ssrn.com/abstract=3682660 or http://dx.doi.org/10.2139/ssrn.3682660

Caio Vigo Pereira (Contact Author)

University of Kansas - Department of Economics ( email )

1300 Sunnyside Drive
Lawrence, KS 66045-7585
United States

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