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
收藏
页码：193 / 207
页数：15
相关论文
共 50 条
12345