A Multi-level Monte Carlo Simulation for Invariant Distribution of Markovian Switching Lévy-Driven SDEs with Super-Linearly Growth Coefficients

This paper concerns the numerical approximation for the invariant distribution of Markovian switching Lévy-driven stochastic differential equations. By combining the tamed-adaptive Euler-Maruyama scheme with the Multi-level Monte Carlo method, we propose an approximation scheme that can be applied t...

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Veröffentlicht in:Methodology and computing in applied probability Jg. 27; H. 4; S. 79
Hauptverfasser: Nguyen, Hoang-Viet, Kieu, Trung-Thuy, Luong, Duc-Trong, Ngo, Hoang-Long, Tran, Ngoc Khue
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.12.2025
Springer Nature B.V
Schlagworte:
Approximation
Business and Management
Differential equations
Diffusion coefficient
Economics
Electrical Engineering
Invariants
Life Sciences
Mathematics and Statistics
Monte Carlo simulation
Numerical analysis
Partial differential equations
Statistics
Super-linearly growth coefficient
Tamed-adaptive Euler-Maruyama scheme
60H10
65C30
Invariant measure
Multi-level Monte-Carlo
Markovian switching
ISSN:1387-5841, 1573-7713
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Zusammenfassung:This paper concerns the numerical approximation for the invariant distribution of Markovian switching Lévy-driven stochastic differential equations. By combining the tamed-adaptive Euler-Maruyama scheme with the Multi-level Monte Carlo method, we propose an approximation scheme that can be applied to stochastic differential equations with super-linear growth drift and diffusion coefficients.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1387-5841
1573-7713
DOI:10.1007/s11009-025-10205-2