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

Xin Liu (), Jiang Wu (), Chen Yang () and Wenjun Jiang ()
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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

Risks, 2018, vol. 6, issue 4, 1-26

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.

Keywords: maximal tail dependence; clustering; financial time series; weighted cuts; copula (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 M2 M4 K2 (search for similar items in EconPapers)
Date: 2018
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Handle: RePEc:gam:jrisks:v:6:y:2018:i:4:p:115-:d:174402