Convergence of tamed Euler schemes for a class of stochastic evolution equations

By István Gyöngy, Sotirios Sabanis, David Siska. Stochastics and Partial Differential Equations: Analysis and Computations, 2014

55 citations recorded by the source corpus.

We prove stability and convergence of a full discretization for a class of stochastic evolution equations with super-linearly growing operators appearing in the drift term. This is done by using the recently developed tamed Euler method, which employs a fully explicit time stepping, coupled with a Galerkin scheme for the spatial discretization.

Subfields: Computational Theory and Mathematics, Finance, Modeling and Simulation

View Paper: https://www.semanticscholar.org/paper/0799213bd3f1ccd4f5c5da73c22196716aac8fa7

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In Computational Theory and Mathematics

1. Arnulf Jentzen

In Finance

1. Primovz Puvsnik
2. Arnulf Jentzen
3. Peter E. Kloeden
4. Xuerong Mao
5. Sotirios Sabanis

In Modeling and Simulation

1. Arnulf Jentzen

Citations in the corpus

• Strong convergence rates for an explicit numerical approximation method for stochastic evolution equations with non-globally Lipschitz continuous nonlinearities (2015, background): “[8], in Hutzenthaler & Jentzen [12, equation (3.”