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Risks 2018, 6(4), 115; https://doi.org/10.3390/risks6040115

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

1
School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai 200433, China
2
School of Economics, Central University of Finance and Economics, Beijing 100081, China
3
Department of Insurance and Actuary, Wuhan University, Wuhan 430072, Hubei, China
4
Department of Statistical and Actuarial Sciences, University of Western Ontario, London, ON N6A 5B7, Canada
*
Author to whom correspondence should be addressed.
Received: 12 August 2018 / Revised: 26 September 2018 / Accepted: 27 September 2018 / Published: 9 October 2018
(This article belongs to the Special Issue Risk, Ruin and Survival: Decision Making in Insurance and Finance)
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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. View Full-Text
Keywords: maximal tail dependence; clustering; financial time series; weighted cuts; copula maximal tail dependence; clustering; financial time series; weighted cuts; copula
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Liu, X.; Wu, J.; Yang, C.; Jiang, W. A Maximal Tail Dependence-Based Clustering Procedure for Financial Time Series and Its Applications in Portfolio Selection. Risks 2018, 6, 115.

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