A tail index estimation for long memory processes

IF 0.9 4区数学 Q3 STATISTICS & PROBABILITY

Metrika Pub Date : 2023-12-20 DOI:10.1007/s00184-023-00938-w

Xiao Wang, Lihong Wang

引用次数: 0

Abstract

This paper provides a least squares regression estimation of the tail index for long memory processes where the innovations are \(\alpha \)-stable random sequences. The estimate is based on the property of the characteristic function of the process near the origin. The asymptotics of the estimator are obtained by choosing suitable regression samples with the help of the properties of the \(\alpha \)-stable distribution. The numerical simulation and an empirical analysis of financial market data are conducted to assess the finite sample performance of the proposed estimator.

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长记忆过程的尾部指数估算

本文对创新为 \(α \)-稳定随机序列的长记忆过程的尾部指数进行了最小二乘回归估计。该估计基于原点附近过程特征函数的性质。在 \(\α \)-稳定分布性质的帮助下，通过选择合适的回归样本得到了估计器的渐近线。对金融市场数据进行了数值模拟和实证分析，以评估所提出的估计器的有限样本性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Metrika 数学-统计学与概率论

CiteScore

1.50

自引率

14.30%

发文量

审稿时长

6-12 weeks

期刊介绍： Metrika is an international journal for theoretical and applied statistics. Metrika publishes original research papers in the field of mathematical statistics and statistical methods. Great importance is attached to new developments in theoretical statistics, statistical modeling and to actual innovative applicability of the proposed statistical methods and results. Topics of interest include, without being limited to, multivariate analysis, high dimensional statistics and nonparametric statistics; categorical data analysis and latent variable models; reliability, lifetime data analysis and statistics in engineering sciences.