Non-negativity preserving numerical algorithms for stochastic differential equations

Details Non-negativity preserving numerical algorithms for stochastic differential equations Non-negativity preserving numerical algorithms for stochastic differential equations

2005-09-30

arxiv.org/abs/math/0509724v1

Mathematics

Numerical Analysis

23 pages, 7 figures. Figures 6.2 and 6.3 in low resolution due to upload size restrictions. Original resolution at http://gi

Scientific paper

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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