Long-range dependence and rational Gaussian noise

被引：0

作者：

Yang, Yipeng ^{[1
]}

机构：

[1] Univ Houston Clear Lake, Dept Math & Stat, Houston, TX 77058 USA

来源：

STATISTICS | 2024年 / 58卷 / 02期

关键词：

Long-range dependence; rational Gaussian noise; fractional Gaussian noise; S&P 500 daily return; MEMORY; MODEL;

D O I：

10.1080/02331888.2024.2344689

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

We propose a new time series model called Rational Gaussian Noise (rGn) to describe a pattern of long-range dependence. The rGn model is shown to be an extension of the traditional fractional Gaussian noise (fGn). Theoretical formulas such as autocorrelation function, and some properties of rGn are derived and compared to that of fGn. Transformed S&P500 daily excessive return data is used as a case study where parameters for both the rGn and fGn models are estimated.

引用

页码：364 / 382

页数：19

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