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Numerical Approximations of Stochastic Differential Equations with Non-Globally Lipschitz Continuous Coefficients (Paperback)
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Numerical Approximations of Stochastic Differential Equations with Non-Globally Lipschitz Continuous Coefficients (Paperback)
Series: Memoirs of the American Mathematical Society
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Many stochastic differential equations (SDEs) in the literature
have a superlinearly growing nonlinearity in their drift or
diffusion coefficient. Unfortunately, moments of the
computationally efficient Euler-Maruyama approximation method
diverge for these SDEs in finite time. This article develops a
general theory based on rare events for studying integrability
properties such as moment bounds for discrete-time stochastic
processes. Using this approach, the authors establish moment bounds
for fully and partially drift-implicit Euler methods and for a
class of new explicit approximation methods which require only a
few more arithmetical operations than the Euler-Maruyama method.
These moment bounds are then used to prove strong convergence of
the proposed schemes. Finally, the authors illustrate their results
for several SDEs from finance, physics, biology and chemistry.
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