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A Portfolio Diversification Strategy via Tail Dependence Clustering

  • Hao Wang
  • Roberta Pappadà
  • Fabrizio Durante
  • Enrico FoscoloEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 456)

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.

Keywords

Portfolio Selection Membership Degree Tail Dependence Multivariate Time Series Weighted Portfolio 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

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”.

References

  1. 1.
    Coppi R, D’Urso P, Giordani P (2006) Fuzzy \(C\)-Medoids clustering models for time-varying data. In: Bouchon-Meunier B, Coletti G, Yager R (eds) Modern information processing: from theory to applications. Elsevier Science, Amsterdam, pp 195–206CrossRefGoogle Scholar
  2. 2.
    De Luca G, Zuccolotto P (2011) A tail dependence-based dissimilarity measure for financial time series clustering. Adv Data Anal Classif 5(4):323–340MathSciNetCrossRefGoogle Scholar
  3. 3.
    De Luca G, Zuccolotto P (2015) Dynamic tail dependence clustering of financial time series. Stat Pap. doi: 10.1007/s00362-015-0718-7
  4. 4.
    Dobrić J, Frahm G, Schmid F (2013) Dependence of stock returns in bull and bear markets. Depend Model 1:94–110zbMATHGoogle Scholar
  5. 5.
    Durante F, Jaworski P (2010) Spatial contagion between financial markets: a copula-based approach. Appl Stoch Models Bus Ind 26(5):551–564MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Durante F, Pappadà R, Torelli N (2014) Clustering of financial time series in risky scenarios. Adv Data Anal Classif 8:359–376MathSciNetCrossRefGoogle Scholar
  7. 7.
    Durante F, Pappadà R, Torelli N (2015) Clustering of time series via non-parametric tail dependence estimation. Stat Pap 56(3):701–721MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    D’Urso P (2015) Fuzzy clustering. In: Meila M, Murtagh F, Rocci R (eds) Handbook of Cluster Analysis. Hennig C. Chapman & Hall,Google Scholar
  9. 9.
    Haerdle W, Nasekin S, Chuen D, Fai P (2014) TEDAS—Tail Event Driven ASset Allocation. Sfb 649 discussion papers, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany, http://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2014-032.pdf
  10. 10.
    Haerdle W, Chuen D, Nasekin S, Ni X, Petukhina A (2015) Tail event driven ASset allocation: evidence from equity and mutual funds’ markets. Sfb 649 discussion papers, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany, http://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2015-045.pdf
  11. 11.
    Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning, data mining, inference, and prediction, Springer Series in Statistics, 2nd edn. Springer, New YorkGoogle Scholar
  12. 12.
    Kaufman L, Rousseeuw P (1990) Finding groups in data. Applied probability and statistics. Wiley Series in probability and mathematical statistics. John Wiley & Sons Inc., New YorkGoogle Scholar
  13. 13.
    Mantegna R (1999) Hierarchical structure in financial markets. Euro Phys J B 11(1):193–197CrossRefGoogle Scholar
  14. 14.
    Patton AJ (2013) Copula methods for forecasting multivariate time series. In: Elliott G, Timmermann A (eds) Handbook of economic forecasting, vol 2. Elsevier, Oxford, pp 899–960Google Scholar
  15. 15.
    Rockafellar RT, Uryasev S (2000) Optimization of conditional value-at-risk. J Risk 2:21–41Google Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Hao Wang
    • 1
  • Roberta Pappadà
    • 2
  • Fabrizio Durante
    • 3
  • Enrico Foscolo
    • 3
    Email author
  1. 1.School of EconomicsJilin UniversityChangchunChina
  2. 2.Department of Economics, Business, Mathematics and Statistics “Bruno De Finetti”University of TriesteTriesteItaly
  3. 3.Faculty of Economics and ManagementFree University of Bozen-BolzanoBolzanoItaly

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