The effects of errors in means, variances, and correlations on the mean-variance framework. Issue 10 (3rd October 2022)

• Record Type: Journal Article
• Authors: Chung, Munki; Lee, Yongjae; Kim, Jang Ho; Kim, Woo Chang; Fabozzi, Frank J.
• Is Part Of: Quantitative finance. Volume 22:Issue 10(2022)
• Page Start: 1893
• Page End: 1903
• Publication Date: 2022-10-03
• Subjects: Investment analysis -- Mean-variance framework -- Sensitivity analysis -- Parameter estimation -- Uniformly distributed random portfolio; G11 -- C61 -- C80; Finance -- Periodicals; Business mathematics -- Periodicals; Finance -- Mathematical models -- Periodicals; Investments -- Mathematics -- Periodicals; Economics -- Periodicals; Finances -- Modèles mathématiques -- Périodiques; 332.015118
• DOI: 10.1080/14697688.2022.2083009
• Languages: English
• ISSNs: 1469-7688

Abstract

Abstract : The mean-variance (MV) framework has been a fundamental tenet of investment management, yet it has been criticized for being too sensitive to parameter estimation errors. Hence, it is important to understand how the errors in parameters affect the MV framework. Although a number of researchers have studied how errors in parameters affect MV optimal portfolios, these studies do not show the complete picture. The MV framework is a tool for systematic evaluation of investment alternatives based on the risk-return trade-off, and MV optimal portfolios are its outputs. In this study, we investigate the effect of errors in parameters on the entire MV framework. We analyze the Sharpe ratio distribution of all possible portfolios, which represents how investments are evaluated under the risk-return trade-off. While means have been widely considered as the most important parameter in the MV optimization, our full-distributional analyses reveal that correlations mostly dominate other parameters.