Abstract
This article examines the portfolio optimization of hedge funds in the downside risk framework in order to take into account the asymmetry of returns and the behavior of investors towards the risk which are not captured by the mean-variance model. By using the Credit Suisse/Tremont Hedge Fund database, the results showed that the downside risk measures have an impact on the composition of optimal portfolios and on the efficient frontier. The results also showed that the mean-variance model overestimates the risk because of the use of the variance as a risk measure, but the mean- semivariance model of Harlow (1991) underestimates the risk because of the cosemivariances measures used in this model. Likewise, the results obtained proved that the new mean-semivariance model provides a better optimization of hedge funds portfolios because it makes it possible to capture the non-normality of hedge funds strategies and the risk perception of investors which are not taken into account by the mean-variance model and also makes it possible to overcome the problem of inequality of the cosemivariances measures in the mean-semivariance model of Harlow (1991).
Published Version
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