Efficient estimation of stable Lévy process with symmetric jumps

被引：11

作者：

Brouste A. ^{[1
]}

Masuda H. ^{[2
]}

机构：

[1] Laboratoire Manceau de Mathématiques, Le Mans Université Avenue Olivier Messiaen, Le Mans Cedex 9

[2] Faculty of Mathematics, Kyushu University, 744 Motooka Nishi-ku, Fukuoka

来源：

Statistical Inference for Stochastic Processes | 2018年 / 21卷 / 2期

基金：

日本学术振兴会;

关键词：

D O I：

10.1007/s11203-018-9181-0

中图分类号：

学科分类号：

摘要：

Efficient estimation of a non-Gaussian stable Lévy process with drift and symmetric jumps observed at high frequency is considered. For this statistical experiment, the local asymptotic normality of the likelihood is proved with a non-singular Fisher information matrix through the use of a non-diagonal norming matrix. The asymptotic normality and efficiency of a sequence of roots of the associated likelihood equation are shown as well. Moreover, we show that a simple preliminary method of moments can be used as an initial estimator of a scoring procedure, thereby conveniently enabling us to bypass numerically demanding likelihood optimization. Our simulation results show that the one-step estimator can exhibit quite similar finite-sample performance as the maximum likelihood estimator. © 2018, Springer Science+Business Media B.V., part of Springer Nature.

引用

页码：289 / 307

页数：18

共 26 条

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