DETERMINING THE BACKGROUND DRIVING PROCESS OF THE ORNSTEIN-UHLENBECK MODEL

被引：0

作者：

Mariani, Maria C. ^{[1
]}

Asante, Peter K. ^{[2
]}

Kubin, William ^{[2
]}

Tweneboah, Osei K. ^{[3
]}

Beccar-Varela, Maria ^{[1
]}

机构：

[1] Univ Texas El Paso, Dept Math Sci, El Paso, TX 79968 USA

[2] Univ Texas El Paso, Computat Sci Program, El Paso, TX 79968 USA

[3] Ramapo Coll, Dept Data Sci, 505 Ramapo Valley Rd, Mahwah, NJ 07430 USA

来源：

ELECTRONIC JOURNAL OF DIFFERENTIAL EQUATIONS | 2023年

关键词：

Stochastic differential equation; Ito Calculus; Levy Process; Ornstein-Uhlenbeck model; Superposed Ornstein-Uhlenbeck model; Gaussian process; Background driving process (BDP); Diffusion entropy analysis (DEA); long-range correlations; detrended fluctuation analysis (DFA); rescaled range analysis (R/S); LEVY; VOLATILITY;

D O I：

暂无

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this work, we determine appropriate background driving processes for the 3-component superposed Ornstein-Uhlenbeck model by analyzing the fractal characteristics of the data sets using the rescaled range analysis (R/S), the detrended fluctuation analysis (DFA), and the diffusion entropy analysis (DEA).

引用

页码：193 / 207

页数：15

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