The Estimation of Systematic Risk under Differentiated Risk Aversion: A Mean-Extended Gini Approach

The Estimation of Systematic Risk under Differentiated Risk Aversion: A Mean-Extended Gini Approach This paper examines a mean-Gini model of systematic risk estimation that resolves some econometric problems with mean-variance beta estimation and allows for heterogeneous risk aversion across investors. Using the mean-extended Gini (MEG) model, we estimate systematic risks for different degrees of risk aversion. MEG betas are shown to be instrumental variable estimators that provide econometric solutions to biases generated by the estimation of mean-variance (MV) betas. When security returns are not normally distributed, MEG betas are proved to differ from MV betas. We design an econometric test that assesses whether these differences are significant. As an application using daily returns, we estimate MEG and MV betas for U.S. securities. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Review of Quantitative Finance and Accounting Springer Journals

The Estimation of Systematic Risk under Differentiated Risk Aversion: A Mean-Extended Gini Approach

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Publisher
Kluwer Academic Publishers
Copyright
Copyright © 1999 by 1999 Kluwer Academic Publishers, Boston. Manufactured in The Netherlands.
Subject
Finance; Corporate Finance; Accounting/Auditing; Econometrics; Operation Research/Decision Theory
ISSN
0924-865X
eISSN
1573-7179
D.O.I.
10.1023/A:1008348104882
Publisher site
See Article on Publisher Site

Abstract

This paper examines a mean-Gini model of systematic risk estimation that resolves some econometric problems with mean-variance beta estimation and allows for heterogeneous risk aversion across investors. Using the mean-extended Gini (MEG) model, we estimate systematic risks for different degrees of risk aversion. MEG betas are shown to be instrumental variable estimators that provide econometric solutions to biases generated by the estimation of mean-variance (MV) betas. When security returns are not normally distributed, MEG betas are proved to differ from MV betas. We design an econometric test that assesses whether these differences are significant. As an application using daily returns, we estimate MEG and MV betas for U.S. securities.

Journal

Review of Quantitative Finance and AccountingSpringer Journals

Published: Oct 15, 2004

References

  • Nonnormalities and Tests of Asset Pricing Theories.
    Affleck-Graves, J. F.; McDonald, B.

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