V. Bally and G. Pagès, A quantization algorithm for solving multi-dimensional optimal stopping problems, pp.1003-1049, 2003.

V. Bally and D. Talay, The law of the Euler scheme for stochastic differential equations, Probability Theory and Related Fields, vol.8, issue.1, pp.104-105, 1996.
DOI : 10.1007/BF01303802
URL : https://hal.archives-ouvertes.fr/inria-00074427

R. Bompis and E. Gobet, Asymptotic and non asymptotic approximations for option valuation, in Recent Developments in Computational Finance: Foundations, Algorithms and, 2012.

S. Brenner and L. Scott, The mathematical theory of finite element methods, Texts in Applied Mathematics, vol.15, 2008.

H. Bungartz and M. Griebel, Sparse grids, Acta Numerica, vol.13, pp.147-269, 2004.
DOI : 10.1017/S0962492904000182

D. Duffie and P. Glynn, Efficient Monte Carlo Simulation of Security Prices, The Annals of Applied Probability, vol.5, issue.4, pp.897-905, 1995.
DOI : 10.1214/aoap/1177004598

J. Fouque, G. Papanicolaou, R. Sircar, and S. Knut, Multiscale stochastic volatility for equity, interest rate, and credit derivatives, 2011.
DOI : 10.1017/CBO9781139020534

E. Gobet, Euler schemes and half-space approximation for the simulation of diffusion in a domain, ESAIM: Probability and Statistics, vol.5, pp.261-297, 2001.
DOI : 10.1051/ps:2001112

E. Gobet and M. Miri, Weak approximation of averaged diffusion processes, Stochastic Processes and their Applications, 2013.
DOI : 10.1016/j.spa.2013.08.007
URL : https://hal.archives-ouvertes.fr/hal-00618470

S. Graf and H. Luschgy, Foundations of quantization for probability distributions, Lecture Notes in Mathematics, vol.1730, 2000.
DOI : 10.1007/BFb0103945

I. Karatzas and S. Shreve, Brownian motion and stochastic calculus, 1991.
DOI : 10.1007/978-1-4612-0949-2

A. Kebaier, Statistical romberg extrapolation: a new variance reduction method and applications to option pricing., The Annals of Applied Probability, pp.2681-2705, 2005.
URL : https://hal.archives-ouvertes.fr/hal-00693106

P. Kloeden and E. Platen, Numerical solution of stochastic differential equations, Applications of Mathematics, vol.23, 2010.

H. Kunita, Stochastic flows and stochastic differential equations, Cambridge Studies in Advanced Mathematics . 24. Cambridge, 1997.

S. Kusuoka, Approximation of expectation of diffusion processes based on Lie algebra and Malliavin calculus, Adv. Math. Econ, vol.6, issue.6, pp.69-83, 2004.
DOI : 10.1007/978-4-431-68450-3_4

J. Lemor, E. Gobet, and X. Warin, Rate of convergence of an empirical regression method for solving generalized backward stochastic differential equations, Bernoulli, vol.12, issue.5, pp.12-889, 2006.
DOI : 10.3150/bj/1161614951
URL : https://hal.archives-ouvertes.fr/hal-00394976

T. Lyons and N. Victoir, Cubature on Wiener space, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.460, issue.2041, pp.169-198, 2004.
DOI : 10.1098/rspa.2003.1239

S. Ninomiya and N. Victoir, Weak Approximation of Stochastic Differential Equations and Application to Derivative Pricing, Applied Mathematical Finance, vol.29, issue.2, pp.107-121, 2008.
DOI : 10.1016/0020-7225(65)90045-5

D. Nualart, Malliavin calculus and related topics, 2006.
DOI : 10.1007/978-1-4757-2437-0

D. Talay and L. Tubaro, Expansion of the global error for numerical schemes solving stochastic differential equations, Stochastic Analysis and Applications, pp.94-120, 1990.