Kendall Correlation Coefficients for Portfolio Optimization

TL;DR

This paper introduces Kendall-like correlation estimators that improve eigenvector and eigenvalue estimation in portfolio optimization, leading to lower out-of-sample risk in data-limited scenarios.

Contribution

It generalizes Kendall's rank correlation to better estimate correlation matrices, enhancing portfolio risk management in data-scarce environments.

Findings

Kendall-like estimators outperform classical methods in risk reduction.

Zero eigenvalues occur only when assets grow proportionally to data points squared.

Improved eigenvector estimation enhances portfolio optimization accuracy.

Abstract

Markowitz's optimal portfolio relies on the accurate estimation of correlations between asset returns, a difficult problem when the number of observations is not much larger than the number of assets. Using powerful results from random matrix theory, several schemes have been developed to "clean" the eigenvalues of empirical correlation matrices. By contrast, the (in practice equally important) problem of correctly estimating the eigenvectors of the correlation matrix has received comparatively little attention. Here we discuss a class of correlation estimators generalizing Kendall's rank correlation coefficient which improve the estimation of both eigenvalues and eigenvectors in data-poor regimes. Using both synthetic and real financial data, we show that these generalized correlation coefficients yield Markowitz portfolios with lower out-of-sample risk than those obtained with rotationally invariant estimators. Central to these results is a property shared by all Kendall-like estimators but not with classical correlation coefficients: zero eigenvalues only appear when the number of assets becomes proportional to the square of the number of data points.