学术期刊
切换导航
首 页
文 章
期 刊
投 稿
预 印
会 议
书 籍
新 闻
合 作
我 们
按学科分类
Journals by Subject
按期刊分类
Journals by Title
核心OA期刊
Core OA Journal
数学与物理
Math & Physics
化学与材料
Chemistry & Materials
生命科学
Life Sciences
医药卫生
Medicine & Health
信息通讯
Information & Communication
工程技术
Engineering & Technology
地球与环境
Earth & Environment
经济与管理
Economics & Management
人文社科
Humanities & Social Sciences
合作期刊
Cooperation Journals
首页
数学与物理
应用数学进展
Vol. 13 No. 1 (January 2024)
期刊菜单
最新文章
历史文章
检索
领域
编委
投稿须知
文章处理费
最新文章
历史文章
检索
领域
编委
投稿须知
文章处理费
标准布朗运动驱动的 CIR 模型梯形数值方法
The Trapezoidal Numerical Method for the CIR Model Driven by Standard Brownian Motion
DOI:
10.12677/AAM.2024.131044
,
PDF
,
,
,
被引量
作者:
陈凯旋
:辽宁师范大学数学学院,辽宁 大连
关键词:
随机微分方程
;
Lamperti变换
;
梯形数值方法
;
CIR模型
;
强收敛阶
;
Stochastic Di?erential Equations
;
Lamperti Transformation
;
Trapezoidal Numerical Method
;
CIR Model
;
Strong Convergence Order
摘要:
本文针对标准布朗运动驱动的Cox–Ingersoll–Ross (CIR)模型探讨了梯形数值方法的强收敛性。通过Lamperti变换, 将CIR模型转换为具有局部Lipschitz条件的漂移项和具有全局Lipschitz条件的扩散项的新方程. 在适当的条件下,证明了新方程梯形数值方法的保正性和强收敛阶,并通过 Lamperti 逆变换得到了CIR模型数值解的强收敛阶。最后,利用数值模拟结果验证了理论分析。
Abstract:
This paper investigates the strong convergence of the trapezoidal numerical method for the Cox–Ingersoll–Ross (CIR) model driven by standard Brownian motion. Through the Lamperti transformation, the CIR model is transformed into a new equation with a drift term satisfying a local Lipschitz condition and a diffusion term satisfying a global Lipschitz condition. Under suitable conditions, the positivity preservation and strong convergence order of the trapezoidal numerical method for the new equation are proven. Furthermore, the strong convergence order of the numerical solution for the CIR model is obtained through the Lamperti inverse transformation. Finally, the theoretical analysis is validated through numerical simulation results.
文章引用:
陈凯旋. 标准布朗运动驱动的 CIR 模型梯形数值方法[J]. 应用数学进展, 2024, 13(1): 444-452.
https://doi.org/10.12677/AAM.2024.131044
参考文献
[1]
Cox, J.C., Ingersoll Jr., J.E. and Ross, S.A. (1985) A Theory of the Term Structure of Interest Rates. Econometrica, 53, 385-407.
https://doi.org/10.2307/1911242
[2]
Higham, D.J. and Mao, X. (2005) Convergence of Monte Carlo Simulations Involving the Mean-Reverting Square Root Process. Journal of Computational Finance, 8, 35-61.
https://doi.org/10.21314/JCF.2005.136
[3]
Dereich, S., Neuenkirch, A. and Szpruch, L. (2012) An Euler-Type Method for the Strong Approximation of the Cox-Ingersoll-Ross Process. Proceedings of the Royal Society A: Mathe- matical, Physical and Engineering Sciences, 468, 1105-1115.
https://doi.org/10.1098/rspa.2011.0505
[4]
Milstein, G.N. (1994) Numerical Integration of Stochastic Differential Equations. Vol. 313, Springer Science Business Media, Berlin.
https://doi.org/10.1007/978-94-015-8455-5
[5]
Fischer, M. and Nappo, G. (2009) On the Moments of the Modulus of Continuity of Itô Processes. Stochastic Analysis and Applications, 28, 103-122.
https://doi.org/10.1080/07362990903415825
投稿
为你推荐
友情链接
科研出版社
开放图书馆