Disentangling the role of variance and covariance information in portfolio selection problems

Abstract

The covariation among financial asset returns is often a key ingredient used in the construction of optimal portfolios. Estimating covariances from data, however, is challenging due to the potential influence of estimation error, specially in high-dimensional problems, which can impact negatively the performance of the resulting portfolios. We address this question by putting forward a simple approach to disentangle the role of variance and covariance information in the case of mean-variance efficient portfolios. Specifically, mean-variance portfolios can be represented as a two-fund rule: one fund is a fully invested portfolio that depends on diagonal covariance elements, whereas the other is a long-short, self financed portfolio associated with the presence of non-zero off-diagonal covariance elements. We characterize the contribution of each of these two components to the overall performance in terms of out-of-sample returns, risk, risk-adjusted returns and turnover. Finally, we provide an empirical illustration of the proposed portfolio decomposition using both simulated and real market data.

Keywords:

• Inverse covariance matrix
• Minimum variance portfolio
• Reward-to-risk timing
• Tangency portfolio
• Volatility timing

JEL Classifications:

• C58
• G11

Notes

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Ministry of Science, Technology and Innovation – CNPq agency [grant number 303688/2016-5].