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A Maximal Tail Dependence-Based Clustering Procedure for Financial Time Series and Its Applications in Portfolio Selection

Author

Listed:
  • Xin Liu

    (School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai 200433, China)

  • Jiang Wu

    (School of Economics, Central University of Finance and Economics, Beijing 100081, China)

  • Chen Yang

    (Department of Insurance and Actuary, Wuhan University, Wuhan 430072, Hubei, China)

  • Wenjun Jiang

    (Department of Statistical and Actuarial Sciences, University of Western Ontario, London, ON N6A 5B7, Canada)

Abstract

In this paper, we propose a clustering procedure of financial time series according to the coefficient of weak lower-tail maximal dependence (WLTMD). Due to the potential asymmetry of the matrix of WLTMD coefficients, the clustering procedure is based on a generalized weighted cuts method instead of the dissimilarity-based methods. The performance of the new clustering procedure is evaluated by simulation studies. Finally, we illustrate that the optimal mean-variance portfolio constructed based on the resulting clusters manages to reduce the risk of simultaneous large losses effectively.

Suggested Citation

  • Xin Liu & Jiang Wu & Chen Yang & Wenjun Jiang, 2018. "A Maximal Tail Dependence-Based Clustering Procedure for Financial Time Series and Its Applications in Portfolio Selection," Risks, MDPI, vol. 6(4), pages 1-26, October.
    • Handle: RePEc:gam:jrisks:v:6:y:2018:i:4:p:115-:d:174402
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    References listed on IDEAS

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    Cited by:

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    3. Jiandong Ren & Kristina Sendova & Ričardas Zitikis, 2019. "Special Issue “Risk, Ruin and Survival: Decision Making in Insurance and Finance”," Risks, MDPI, vol. 7(3), pages 1-7, September.

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