Monte-Carlo simulation of stochastic differential systems - a geometrical approach

DOI10.1016/j.spa.2007.04.009zbMath1141.60042OpenAlexW2078512968MaRDI QIDQ2476884

Carlos J. S. Alves, Ana Bela Cruzeiro

Publication date: 12 March 2008

Published in: Stochastic Processes and their Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spa.2007.04.009

zbMATH Keywords

stochastic differential equationsMonte Carlo methodsnumerical approximationdiffusion equationsMilstein schemegeometric-numerical approach

Mathematics Subject Classification ID

Monte Carlo methods (65C05) Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10) Computational methods for stochastic equations (aspects of stochastic analysis) (60H35) Numerical solutions to stochastic differential and integral equations (65C30)

Related Items (4)

HIGHER-ORDER RUNGE-KUTTA METHOD FOR ITÔ STOCHASTIC DIFFERENTIAL EQUATIONS WITH A NON-DEGENERATE DIFFUSION MATRIX ⋮ Total variation bound for Milstein scheme without iterated integrals ⋮ TWO-STEP ORDER STRONG METHOD FOR APPROXIMATING STOCHASTIC DIFFERENTIAL EQUATIONS ⋮ Numerical approximation of diffusions in \(\mathbb {R}^d\) using normal charts of a Riemannian manifold

Cites Work

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• A survey of numerical methods for stochastic differential equations
• Numerical error for SDE: Asymptotic expansion and hyperdistributions
• Geometrization of Monte-Carlo numerical analysis of an elliptic operator: Strong approximation
• Probabilistic methods in applied physics
• The law of the Euler scheme for stochastic differential equations. I: Convergence rate of the distribution function
• Expansion of the global error for numerical schemes solving stochastic differential equations

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