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
Online-Zugang:	Volltext
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