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Selection of the right risk measures for portfolio allocation

Nguyen,T 2014, Selection of the right risk measures for portfolio allocation, International journal of monetary economics and finance, vol. 7, no. 2, pp. 135-156, doi: 10.1504/IJMEF.2014.065099.

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Title Selection of the right risk measures for portfolio allocation
Author(s) Nguyen,TORCID iD for Nguyen,T orcid.org/0000-0001-9709-1663
Journal name International journal of monetary economics and finance
Volume number 7
Issue number 2
Start page 135
End page 156
Publisher Inderscience Publishers
Place of publication Olney, England
Publication date 2014
ISSN 1752-0479
1752-0487
Summary  An optimisation framework is proposed to enable investors to select the right risk measures in portfolio selection. Verification is deployed by performing experiments in developed markets (e.g., the US stock market), emerging markets (e.g., the South Korean stock market) and global investments. A preselection procedure dealing with large datasets is also introduced to eliminate stocks that have low diversification potential before running the portfolio optimisation model. Portfolios are evaluated by four performance indices, i.e., the Sortino ratio, the Sharpe ratio, the Stutzer performance index, and the Omega measure. Experimental results demonstrate that high performance and also well-diversified portfolios are obtained if modified value-at-risk, variance, or semi-variance is concerned whereas emphasising only skewness, kurtosis or higher moments in general produces low performance and poorly diversified portfolios. In addition, the preselection applied to large datasets results in portfolios that have not only high performance but also high diversification degree.
Language eng
DOI 10.1504/IJMEF.2014.065099
Field of Research 010205 Financial Mathematics
089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970101 Expanding Knowledge in the Mathematical Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, Inderscience Publishers
Persistent URL http://hdl.handle.net/10536/DRO/DU:30071830

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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