NDLI: The M-bootstrap estimation of heavy-tailed index and empirical analysis of Chinese stock markets

Content Provider	IEEE Xplore Digital Library
Author	Liu Wei-qi
Copyright Year	2008
Description	Author affiliation: Sch. of Manage., Shanxi Univ., Taiyuan (Liu Wei-qi)
Abstract	Estimating the tail index of a heavy-tailed distribution depends on the choice of the number k of upper order statistics used in the estimation. In this paper, we reviewed estimating tail index of the heavy-tailed distribution historic course. We summarized selecting k from the heavy-tailed index to the research state and discussed the sum-plot method and bootstrap method of selecting k from heavy-tailed index estimating in detail. And improved the bootstrap method which proposed by Hall, which is called the M-bootstrap method. And we used the above three methods to carry on the Monte-Carlo simulation to the known heavy-tailed distribution, studied their feasibility, compared them with their robust. The results of these three methods are satisfied. Sum-plot method and M-bootstrap method arenpsilat impacted by outliers. Afterwards we made empirical analysis based on Shanghai Stock Index and Shenzhen Component Index data, the computed result indicated that Shanghai Stock Index and Shenzhen Component Index returns ratio is thick-tailed and expose right skew, right tail heavier on left tail.
Starting Page	1275
Ending Page	1285
File Size	675107
Page Count	11
File Format	PDF
ISBN	9781424423873
DOI	10.1109/ICMSE.2008.4669072
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-09-10
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Statistical analysis risk evaluation Hill’s estimation Conference management Educational institutions Probability distribution heavy-tailed index Convergence Engineering management Statistical distributions Tail bootstrap method heavy-tailed distribution Random variables Stock markets
Content Type	Text
Resource Type	Article

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