On robust tail index estimation for linear long-memory processes

被引：8
作者：
Beran, Jan [1 ]
Das, Bikramjit [2 ]
Schell, Dieter
机构：
[1] Univ Konstanz, Dept Math & Stat, D-78457 Constance, Germany
[2] Swiss Fed Inst Technol, Zurich, Switzerland
来源：
JOURNAL OF TIME SERIES ANALYSIS | 2012年 / 33卷 / 03期
关键词：
Long memory; infinite variance; tail index; M-estimation; RANGE DEPENDENT SEQUENCES; SAMPLING WINDOW METHOD; CENTRAL LIMIT-THEOREMS; EMPIRICAL PROCESS; ASYMPTOTIC NORMALITY; MOVING AVERAGES; HILL ESTIMATOR; CONVERGENCE; PARAMETER; BOOTSTRAP;
D O I：
10.1111/j.1467-9892.2011.00774.x
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
We consider robust estimation of the tail index a for linear long-memory processes with i.i.d. innovations ej following a symmetric a-stable law (1 < a < 2) and coefficients aj similar to c.j-beta. Estimates based on the left and right tail respectively are obtained together with a combined statistic with improved efficiency, and a test statistic comparing both tails. Asymptotic results are derived. Simulations illustrate the finite sample performance.
引用
收藏
页码：406 / 423
页数：18
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