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

doi:10.3390/risks6040115

CORC > 上海财经大学 > 上海财经大学

	A Maximal Tail Dependence-Based Clustering Procedure for Financial Time Series and Its Applications in Portfolio Selection
	Liu, Xin 1; Wu, Jiang 2; Yang, Chen 3; Jiang, Wenjun 4
刊名	RISKS
	2018-12
卷号	6 期号:4
关键词	maximal tail dependence clustering financial time series weighted cuts copula
ISSN号	2227-9091
DOI	10.3390/risks6040115
英文摘要	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.
WOS研究方向	Business & Economics
语种	英语
出版者	MDPI
WOS记录号	WOS:000455642400012
内容类型	期刊论文
源URL	[http://10.2.47.112/handle/2XS4QKH4/444]
专题	上海财经大学
通讯作者	Wu, Jiang
作者单位	1.Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai 200433, Peoples R China; 2.Cent Univ Finance & Econ, Sch Econ, Beijing 100081, Peoples R China; 3.Wuhan Univ, Dept Insurance & Actuary, Wuhan 430072, Hubei, Peoples R China; 4.Univ Western Ontario, Dept Stat & Actuarial Sci, London, ON N6A 5B7, Canada
推荐引用方式 GB/T 7714	Liu, Xin,Wu, Jiang,Yang, Chen,et al. A Maximal Tail Dependence-Based Clustering Procedure for Financial Time Series and Its Applications in Portfolio Selection[J]. RISKS,2018,6(4).
APA	Liu, Xin,Wu, Jiang,Yang, Chen,&Jiang, Wenjun.(2018).A Maximal Tail Dependence-Based Clustering Procedure for Financial Time Series and Its Applications in Portfolio Selection.RISKS,6(4).
MLA	Liu, Xin,et al."A Maximal Tail Dependence-Based Clustering Procedure for Financial Time Series and Its Applications in Portfolio Selection".RISKS 6.4(2018).