Convergence rates of the Semi-Discrete method for stochastic differential equations
Teoriâ slučajnyh processov, Tome 24 (2019) no. 2, pp. 89-100.

Voir la notice de l'article provenant de la source Math-Net.Ru

We study the convergence rates of the semi-discrete (SD) method originally proposed in Halidias (2012), Semi-discrete approximations for stochastic differential equations and applications, International Journal of Computer Mathematics, 89(6). The SD numerical method was originally designed mainly to reproduce qualitative properties of nonlinear stochastic differential equations (SDEs). The strong convergence property of the SD method has been proved, but except for certain classes of SDEs, the order of the method was not studied. We study the order of ${\mathcal L}^2$-convergence and show that it can be arbitrarily close to $1/2.$ The theoretical findings are supported by numerical experiments.
Keywords: Explicit Numerical Scheme, Semi-Discrete Method, non-linear SDEs Stochastic Differential Equations, Boundary Preserving Numerical Algorithm.
@article{THSP_2019_24_2_a6,
     author = {I. S. Stamatiou and N. Halidias},
     title = {Convergence rates of the {Semi-Discrete} method for stochastic differential equations},
     journal = {Teori\^a slu\v{c}ajnyh processov},
     pages = {89--100},
     publisher = {mathdoc},
     volume = {24},
     number = {2},
     year = {2019},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/THSP_2019_24_2_a6/}
}
TY  - JOUR
AU  - I. S. Stamatiou
AU  - N. Halidias
TI  - Convergence rates of the Semi-Discrete method for stochastic differential equations
JO  - Teoriâ slučajnyh processov
PY  - 2019
SP  - 89
EP  - 100
VL  - 24
IS  - 2
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/THSP_2019_24_2_a6/
LA  - en
ID  - THSP_2019_24_2_a6
ER  - 
%0 Journal Article
%A I. S. Stamatiou
%A N. Halidias
%T Convergence rates of the Semi-Discrete method for stochastic differential equations
%J Teoriâ slučajnyh processov
%D 2019
%P 89-100
%V 24
%N 2
%I mathdoc
%U http://geodesic.mathdoc.fr/item/THSP_2019_24_2_a6/
%G en
%F THSP_2019_24_2_a6
I. S. Stamatiou; N. Halidias. Convergence rates of the Semi-Discrete method for stochastic differential equations. Teoriâ slučajnyh processov, Tome 24 (2019) no. 2, pp. 89-100. http://geodesic.mathdoc.fr/item/THSP_2019_24_2_a6/

[1] S. Ermakov, A. Pogosian, “On solving stochastic differential equations”, Monte Carlo Methods and Applications, 25:2 (2019), 155–161 | DOI | MR | Zbl

[2] W. Fang, M. B. Giles, “Adaptive Euler–Maruyama Method for SDEs with Non-globally Lipschitz Drift”, Monte Carlo and Quasi-Monte Carlo Methods, eds. A. B. Owen, P.W. Glynn, Springer International Publishing, Cham, 217–234 | MR | Zbl

[3] N. Halidias, “Semi-discrete approximations for stochastic differential equations and applications”, International Journal of Computer Mathematics, 89:6 (2012), 780–794 | DOI | MR | Zbl

[4] N. Halidias, “A novel approach to construct numerical methods for stochastic differential equations”, Numerical Algorithms, 66:1 (2014), 79–87 | DOI | MR | Zbl

[5] N. Halidias, “Construction of positivity preserving numerical schemes for some multidimensional stochastic differential equations”, Discrete and Continuous Dynamical Systems - Series B, 20:1 (2015), 153–160 | DOI | MR | Zbl

[6] N. Halidias, “Constructing positivity preserving numerical schemes for the two-factor {CIR} model”, Monte Carlo Methods and Applications, 21:4 (2015), 313–323 | DOI | MR | Zbl

[7] N. Halidias, I. S. Stamatiou, “On the {N}umerical {S}olution of {S}ome {N}on-{L}inear {S}tochastic {D}ifferential {E}quations {U}sing the {S}emi-{D}iscrete {M}ethod”, Computational Methods in Applied Mathematics, 16:1 (2016), 105–132 | DOI | MR | Zbl

[8] N. Halidias, I. S. Stamatiou, “Approximating Explicitly the Mean-Reverting CEV Process”, Journal of Probability and Statistics, 2015, 513137, 20 pp. | DOI | MR

[9] L. Hu, X. Li, X. Mao, “Convergence rate and stability of the truncated Euler-Maruyama method for stochastic differential equations”, Journal of Computational and Applied Mathematics, 337 (2018), 274–289 | DOI | MR | Zbl

[10] M. Hutzenthaler, A. Jentzen, Numerical approximations of stochastic differential equations with non-globally {L}ipschitz continuous coefficients, Memoirs of the American Mathematical Society, 236, no. 1112, 2015 | DOI | MR

[11] M. Hutzenthaler, A. Jentzen, P. E. Kloeden, “Strong and weak divergence in finite time of {E}uler's method for stochastic differential equations with non-globally {L}ipschitz continuous coefficients”, Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, ed. O. R. Auth, The Royal Society, 1563–1576 | MR | Zbl

[12] I. Karatzas, S. E. Shreve, Brownian motion and stochastic calculus, Springer-Verlag, New York, 1988 | MR | Zbl

[13] C. Kelly, G. Lord, “Adaptive time-stepping strategies for nonlinear stochastic systems”, IMA Journal of Numerical Analysis, 2017 | MR

[14] C. Kelly, A. Rodkina, E. M. Rapoo, “Adaptive timestepping for pathwise stability and positivity of strongly discretised nonlinear stochastic differential equations”, Journal of Computational and Applied Mathematics, 334 (2018), 39–57 | DOI | MR | Zbl

[15] P. E. Kloeden, E. Platen, Numerical {S}olution of {S}tochastic {D}ifferential {E}quations, corrected 2nd printing, Springer-Verlag, Berlin, 1995 | MR

[16] X. Mao, Stochastic differential equations and applications, 2nd edition, Horwood Publishing, Chichester, 2007 | MR | Zbl

[17] X. Mao, “The truncated Euler-Maruyama method for stochastic differential equations”, Journal of Computational and Applied Mathematics, 290 (2015), 370–384 | MR | Zbl

[18] X. Mao, “Convergence rates of the truncated Euler-Maruyama method for stochastic differential equations”, Journal of Computational and Applied Mathematics, 296 (2016), 362–375 | MR | Zbl

[19] A. Neuenkirch, L. Szpruch, “First order strong approximations of scalar {SDE}s defined in a domain”, Numerische Mathematik, 128:1 (2014), 103–136 | MR | Zbl

[20] S. Sabanis, “Euler approximations with varying coefficients: the case of superlinearly growing diffusion coefficients”, Annals of Applied Probability, 26:4 (2016), 2083–2105 | DOI | MR | Zbl

[21] I. S. Stamatiou, “A boundary preserving numerical scheme for the {W}right-{F}isher model”, Journal of Computational and Applied Mathematics, 328 (2018), 132–150 | DOI | MR | Zbl

[22] I. S. Stamatiou, “An explicit positivity preserving numerical scheme for CIR/CEV type delay models with jump”, Journal of Computational and Applied Mathematics, 360 (2019), 78–98 | DOI | MR | Zbl

[23] M. V. Tretyakov, Z. Zhang, “A fundamental mean-square convergence theorem for SDEs with locally Lipschitz coefficients and its applications”, SIAM Journal on Numerical Analysis, 51:6 (2013), 3135–3162 | DOI | MR | Zbl

[24] W. Wagner, “Monte Carlo evaluation of functionals of solutions of stochastic differential equations. Variance reduction and numerical examples”, Stochastic Analysis and Applications, 6:4 (1988), 447–468 | DOI | MR | Zbl