Non-negativity preserving numerical algorithms for stochastic differential equations
Numerical Analysis
2007-05-23 v1 Probability
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.
Cite
@article{arxiv.math/0509724,
title = {Non-negativity preserving numerical algorithms for stochastic differential equations},
author = {Esteban Moro and Henri Schurz},
journal= {arXiv preprint arXiv:math/0509724},
year = {2007}
}
Comments
23 pages, 7 figures. Figures 6.2 and 6.3 in low resolution due to upload size restrictions. Original resolution at http://gisc.uc3m.es/~moro/profesional.html