{"title":"An explicit positivity-preserving scheme for the Heston 3/2-model with order-one strong convergence","authors":"Xiaojuan Wu , Siqing Gan","doi":"10.1016/j.cnsns.2024.108372","DOIUrl":null,"url":null,"abstract":"<div><div>This article is concerned with numerical approximations of the Heston 3/2-model from mathematical finance, which takes values in <span><math><mrow><mo>(</mo><mn>0</mn><mo>,</mo><mi>∞</mi><mo>)</mo></mrow></math></span> and possesses superlinearly growing drift and diffusion coefficients. To discretize the SDE model, a new Milstein-type scheme is proposed, which can be explicitly solved and is positivity-preserving unconditionally, i.e., for any time step-size <span><math><mrow><mi>h</mi><mo>></mo><mn>0</mn></mrow></math></span>. Furthermore, a mean-square convergence rate of order one is proved in the non-globally Lipschitz regime, which is highly non-trivial, by noting that the diffusion coefficient grows super-linearly. The above theoretical results can be then used to justify the multilevel Monte Carlo (MLMC) methods for approximating expectations of some functions of the solution to the Heston 3/2-model. Indeed, the unconditional positivity-preserving property is particularly desirable in the MLMC setting, where large discretization time steps are used. The obtained order-one convergence in turn promises the desired relevant variance of the multilevel estimator and justifies the optimal complexity <span><math><mrow><mi>O</mi><mrow><mo>(</mo><msup><mrow><mi>ϵ</mi></mrow><mrow><mo>−</mo><mn>2</mn></mrow></msup><mo>)</mo></mrow></mrow></math></span> for the MLMC approach, where <span><math><mrow><mi>ϵ</mi><mo>></mo><mn>0</mn></mrow></math></span> is the required target accuracy. Numerical experiments are finally reported to confirm the above results.</div></div>","PeriodicalId":50658,"journal":{"name":"Communications in Nonlinear Science and Numerical Simulation","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Communications in Nonlinear Science and Numerical Simulation","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1007570424005574","RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, APPLIED","Score":null,"Total":0}
引用次数: 0
Abstract
This article is concerned with numerical approximations of the Heston 3/2-model from mathematical finance, which takes values in and possesses superlinearly growing drift and diffusion coefficients. To discretize the SDE model, a new Milstein-type scheme is proposed, which can be explicitly solved and is positivity-preserving unconditionally, i.e., for any time step-size . Furthermore, a mean-square convergence rate of order one is proved in the non-globally Lipschitz regime, which is highly non-trivial, by noting that the diffusion coefficient grows super-linearly. The above theoretical results can be then used to justify the multilevel Monte Carlo (MLMC) methods for approximating expectations of some functions of the solution to the Heston 3/2-model. Indeed, the unconditional positivity-preserving property is particularly desirable in the MLMC setting, where large discretization time steps are used. The obtained order-one convergence in turn promises the desired relevant variance of the multilevel estimator and justifies the optimal complexity for the MLMC approach, where is the required target accuracy. Numerical experiments are finally reported to confirm the above results.
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Nonlinear differential or delay equations, Lie group analysis and asymptotic methods, Discontinuous systems, Fractals, Fractional calculus and dynamics, Nonlinear effects in quantum mechanics, Nonlinear stochastic processes, Experimental nonlinear science, Time-series and signal analysis, Computational methods and simulations in nonlinear science and engineering, Control of dynamical systems, Synchronization, Lyapunov analysis, High-dimensional chaos and turbulence, Chaos in Hamiltonian systems, Integrable systems and solitons, Collective behavior in many-body systems, Biological physics and networks, Nonlinear mechanical systems, Complex systems and complexity.
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