The estimation of systematic risk under differentiated risk aversion: A mean-extended gini approach

Russell B. Gregory-Allen; Haim Shalit

doi:10.1023/a:1008348104882

The estimation of systematic risk under differentiated risk aversion: A mean-extended gini approach

Russell B. Gregory-Allen, Haim Shalit

Department of Economics

Research output: Contribution to journal › Article › peer-review

7 Scopus citations

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.

Original language	English
Pages (from-to)	135-158
Number of pages	24
Journal	Review of Quantitative Finance and Accounting
Volume	12
Issue number	2
DOIs	https://doi.org/10.1023/a:1008348104882
State	Published - 1 Jan 1999

Keywords

Beta
Instrumental variable estimation
Mean-Gini
Normality test

ASJC Scopus subject areas

Accounting
Business, Management and Accounting (all)
Finance

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AB - 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.

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KW - Normality test

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The estimation of systematic risk under differentiated risk aversion: A mean-extended gini approach

Abstract

Keywords

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