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Non-negativity preserving numerical algorithms for stochastic differential equations

Esteban Moro·Henri Schurz
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Abstract

Construction of splitting-step methods and properties of related non-negativity and boundary preserving numerical algorithms for solving stochastic differential equations (SDEs) of Ito-type are discussed. We present convergence proofs for a newly designed splitting-step algorithm and simulation studies for numerous numerical examples ranging from stochastic dynamics occurring in asset pricing theory in mathematical finance (SDEs of CIR and CEV models) to measure-valued diffusion and superBrownian motion (SPDEs) as met in biology and physics.

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Esteban Moro, Henri Schurz. 2005-09-30. Non-negativity preserving numerical algorithms for stochastic differential equations. https://arxiv.org/abs/math/0509724

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