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
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.
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页码:364 / 382
页数:19
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