Portfolio Efficiency with High-Dimensional Data as Conditioning Information
Caio Vigo Pereira
No 202015, WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS from University of Kansas, Department of Economics
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. (search for similar items in EconPapers)
JEL-codes: C32 C38 G11 G17 (search for similar items in EconPapers)
Date: 2020-09, Revised 2020-09
New Economics Papers: this item is included in nep-fmk and nep-ore
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Journal Article: Portfolio efficiency with high-dimensional data as conditioning information (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:kan:wpaper:202015
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