Exact simulation of Ornstein-Uhlenbeck tempered stable processes

被引:13
|
作者
Qu, Yan [1 ]
Dassios, Angelos [2 ]
Zhao, Hongbiao [3 ,4 ]
机构
[1] Peking Univ, Sch Math Sci, Beijing 100871, Peoples R China
[2] London Sch Econ & Polit Sci, Dept Stat, Houghton St, London WC2A 2AE, England
[3] Shanghai Univ Finance & Econ, Sch Stat & Management, 777 Guoding Rd, Shanghai 200433, Peoples R China
[4] Shanghai Inst Int Finance & Econ, 777 Guoding Rd, Shanghai 200433, Peoples R China
基金
中国国家自然科学基金;
关键词
Monte Carlo simulation; exact simulation; non-Gaussian Ornstein-Uhlenbeck process; tempered stable subordinator; tempered stable OU process; OU tempered stable process; STOCHASTIC VOLATILITY MODELS; EFFICIENT SIMULATION; CONTINUOUS-TIME; DRIVEN; INFERENCE; DISTRIBUTIONS;
D O I
10.1017/jpr.2020.92
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
There are two types of tempered stable (TS) based Ornstein-Uhlenbeck (OU) processes: (i) the OU-TS process, the OU process driven by a TS subordinator, and (ii) the TS-OU process, the OU process with TS marginal law. They have various applications in financial engineering and econometrics. In the literature, only the second type under the stationary assumption has an exact simulation algorithm. In this paper we develop a unified approach to exactly simulate both types without the stationary assumption. It is mainly based on the distributional decomposition of stochastic processes with the aid of an acceptance-rejection scheme. As the inverse Gaussian distribution is an important special case of TS distribution, we also provide tailored algorithms for the corresponding OU processes. Numerical experiments and tests are reported to demonstrate the accuracy and effectiveness of our algorithms, and some further extensions are also discussed.
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页码:347 / 371
页数:25
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