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

Risks

Chen Yang¹  Wenjun Jiang²  Jiang Wu³  Xin Liu⁴ 

[1] Department of Insurance and Actuary, Wuhan University, Wuhan 430072, Hubei, China;Department of Statistical and Actuarial Sciences, University of Western Ontario, London, ON N6A 5B7, Canada;School of Economics, Central University of Finance and Economics, Beijing 100081, China;School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai 200433, China;

关键词: maximal tail dependence;    clustering;    financial time series;    weighted cuts;    copula;   

DOI  :  10.3390/risks6040115

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown