Abstract
We provide a two-stage portfolio selection procedure in order to increase the diversification benefits in a bear market. By exploiting tail dependence-based risky measures, a cluster analysis is carried out for discerning between assets with the same performance in risky scenarios. Then, the portfolio composition is determined by fixing a number of assets and by selecting only one item from each cluster. Empirical calculations on the EURO STOXX 50 prove that investing on selected assets in trouble periods may improve the performance of risk-averse investors.
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Acknowledgments
The first author acknowledges the support of the Major Program of the National Social Science Foundation of China (No. 15ZDA017), and the support of Jilin University via the “Fundamental Research Funds for the Central Universities” (No. 450060522110) and via “Young Academic Leaders Training Program” (No. 2015FRLX07). The second author acknowledges the support of the Department of Economics, Business, Mathematics and Statistics “Bruno De Finetti” (University of Trieste, Italy), via the project “FRA”. The third and fourth author acknowledge the support of the Faculty of Economics and Management (Free University of Bozen-Bolzano, Italy), via the project “COCCO”.
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Wang, H., Pappadà, R., Durante, F., Foscolo, E. (2017). A Portfolio Diversification Strategy via Tail Dependence Clustering. In: Ferraro, M., et al. Soft Methods for Data Science. SMPS 2016. Advances in Intelligent Systems and Computing, vol 456. Springer, Cham. https://doi.org/10.1007/978-3-319-42972-4_63
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DOI: https://doi.org/10.1007/978-3-319-42972-4_63
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