 Portfolio efficiency with high-dimensional data as conditioning informationInternational Review of Financial Analysis, 2021, vol. 77, issue C Abstract: In this paper, we build efficient portfolios using different frameworks proposed in the literature and drawing upon several datasets that contain an increasing number of predictors as conditioning information. We carry an extensive empirical study to investigate approaches that 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 factor models, such as the principal component regression and the partial least squares, provides better out-of-sample results as measured by Sharpe ratios, implied Sharpe ratios, and higher certainty equivalent returns (CER). Keywords: Dimensionality reduction; Shrinkage; Efficient portfolios; Principal components regression (PCR); Partial least squares (PLS); Three-pass regression filter (3PRF); Ridge regression; LASSO (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:77:y:2021:i:c:s1057521921001460 DOI: 10.1016/j.irfa.2021.101811 Access Statistics for this article International Review of Financial Analysis is currently edited by B.M. Lucey More articles in International Review of Financial Analysis from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.