P. [. Anthony and . Bartlett, Neural network learning: Theoretical foundations, 2009.
DOI : 10.1017/CBO9780511624216

S. [. Armenti and . Crépey, XVA metrics for CCP optimisation Working paper available at https://math.maths.univ-evry.fr/crepey, Albanese, S. Caenazzo, and S. Crépey. Credit, funding, margin, and capital valuation adjustments for bilateral portfolios. Probability, Uncertainty and Quantitative Risk, p.7, 2017.

P. Artzner, F. Delbaen, J. M. Eber, and D. Heath, Coherent Measures of Risk, Mathematical Finance, vol.9, issue.3, pp.203-228, 1999.
DOI : 10.1111/1467-9965.00068

S. A. Agarwal, E. De-marco, J. Gobet, F. López-salas, A. Noubiagain et al., Numerical approximations of McKean anticipative backward stochastic differential equations arising in Initial Margin requirements, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01686952

S. [. Agarwal, E. De-marco, G. Gobet, and . Liu, Rare event simulation related to financial risks: efficient estimation and sensitivity analysis, p.1219616, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01219616

A. Agarwal, S. De-marco, E. Gobet, and G. Liu, Study of new rare event simulation schemes and their application to extreme scenario generation, Mathematics and Computers in Simulation, vol.143
DOI : 10.1016/j.matcom.2017.05.004

URL : https://hal.archives-ouvertes.fr/hal-01249625

. Winterschool-lunteren, -23-24 E. Gobet -Simulation of (nested/extreme) risks in finance, Mathematics and Computers in Simulation, vol.143, pp.89-98, 2018.

P. [. Asmussen and . Glynn, Stochastic simulation: Algorithms and analysis. Stochastic Modelling and Applied Probability 57, 2007.

L. Abbas-turki, S. Crépey, and B. Diallo, XVA principles, nested Monte Carlo, and GPU optimizations, 2017.

N. [. Bardou, G. Frikha, and . Pages, Computing VaR and CVaR using stochastic approximation and adaptive unconstrained importance sampling. Monte-Carlo Methods and Applications, pp.173-210, 2009.
DOI : 10.1515/mcma.2009.011

URL : https://hal.archives-ouvertes.fr/hal-00497588

. Bgg-+-16-]-c.-e, M. Bréhier, L. Gazeau, T. Goudenege, M. Lelièvre et al., Unbiasedness of some generalized adaptive multilevel splitting algorithms, The Annals of Applied Probability, vol.26, issue.6, pp.3559-3601, 2016.

J. [. Bhamidi, C. Y. Hannig, J. Lee, and . Nolen, The importance sampling technique for understanding rare events in ??????Erd??s???R??nyi random graphs, Electronic Journal of Probability, vol.20, issue.0, 2013.
DOI : 10.1214/EJP.v20-2696

M. Chiachio, J. L. Beck, J. Chiachio, and G. Rus, Approximate Bayesian Computation by Subset Simulation, SIAM Journal on Scientific Computing, vol.36, issue.3, pp.1339-1358, 2014.
DOI : 10.1137/130932831

URL : http://arxiv.org/pdf/1404.6225

R. Carmona and S. Crépey, PARTICLE METHODS FOR THE ESTIMATION OF CREDIT PORTFOLIO LOSS DISTRIBUTIONS, International Journal of Theoretical and Applied Finance, vol.16, issue.04, pp.577-602, 2010.
DOI : 10.1214/105051605000000566

F. Cérou, P. , and A. Guyader, A nonasymptotic theorem for unnormalized Feynman???Kac particle models, Annales de l'Institut Henri Poincar??, Probabilit??s et Statistiques, vol.47, issue.3, pp.629-649, 2011.
DOI : 10.1214/10-AIHP358

R. [. Crépey, W. Élie, S. Sabbagh, and . Song, When capital is a funding source: The XVA anticipated BSDEs. preprint, 2017.

G. [. Crépey, E. Fort, U. Gobet, . [. Stazhynski, J. P. Carmona et al., Uncertainty quantification for stochastic approximation limits using chaos expansion. hal-01629952 Interacting particle systems for the computation of rare credit portfolio losses Adaptive multilevel splitting for rare event analysis, Finance Stoch. Stoch. Anal. Appl, vol.13, issue.252, pp.613-633417, 2007.

P. [. Cérou, T. Del-moral, A. Furon, and . Guyader, Sequential Monte Carlo for rare event estimation, Statistics and Computing, vol.22, issue.4, pp.795-808, 2012.
DOI : 10.1017/CBO9780511802256

]. P. Del04, Feynman-Kac Formulae: Genealogical and Interacting Particle Systems with Applications, 2004.

]. L. Dev86 and . Devroye, Nonuniform random variate generation, 1986.

J. [. Del-moral and . Garnier, Genealogical particle analysis of rare events, The Annals of Applied Probability, vol.15, issue.4, pp.2496-2534, 2005.
DOI : 10.1214/105051605000000566

URL : https://hal.archives-ouvertes.fr/hal-00017928

S. [. Devineau and . Loisel, Construction d'un algorithme d'accélération de la méthode des «simulations dans les simulations» pour le calcul du capital économique solvabilité ii. Bulletin Français d'Actuariat Backward stochastic differential equations in finance, Math. Finance, vol.10, issue.71, pp.188-2211, 1997.

E. [. Fort and . Moulines, Convergence of the monte carlo expectation maximization for curved exponential families An introduction in discrete time, Annals of Statistics, vol.27, pp.1220-1259, 2002.

P. Glasserman, P. Heidelberger, and P. Shahabuddin, Variance Reduction Techniques for Estimating Value-at-Risk, Management Science, vol.46, issue.10, pp.1349-1364, 2000.
DOI : 10.1287/mnsc.46.10.1349.12274

URL : http://www.research.ibm.com/people/b/berger/papers/RC21577.ps

]. M. Gil08 and . Giles, Multilevel Monte-Carlo path simulation, Operation Research, vol.56, pp.607-617, 2008.

]. M. Gil15 and . Giles, Multilevel monte carlo methods, Acta Numerica, vol.24, pp.259-328, 2015.

J. Gatheral, T. Jaisson, M. Rosenbaumgl15-]-e, G. Gobet, and . Liu, Volatility is rough Rare event simulation using reversible shaking transformations, SIAM Scientific Computing, vol.37, issue.5, pp.2295-2316, 2015.

]. P. Gla03 and . Glasserman, Monte-Carlo methods in Financial Engineering, 2003.

A. Gulisashvili and P. Tankov, Tail behavior of sums and differences of log-normal random variables. arXiv preprint arXiv:1309, 2013.

]. J. Guy14 and . Guyon, Path-dependent volatility. Preprint available at http: // ssrn. com/ abstract= 2425048, 2014.

]. S. Hei01 and . Heinrich, Multilevel Monte-Carlo Methods, LSSC '01 Proceedings of the Third International Conference on Large-Scale Scientific Computing Spectral Methods for Uncertainty Quantification. With Applications to Computational Fluid Dynamics. Scientific Computation, pp.58-67, 2001.

B. [. Latuszy?ski, W. Miasojedow, and . Niemiro, Nonasymptotic bounds on the estimation error of MCMC algorithms, Bernoulli, vol.19, issue.5A, pp.2033-2066, 2013.
DOI : 10.3150/12-BEJ442

]. P. Mor13, Mean field simulation for Monte-Carlo integration, volume 126 of Monographs on Statistics and Applied Probability, 2013.

R. [. Meyn and . Tweedie, Markov chains and stochastic stability, 2009.

]. D. Nua06 and . Nualart, Malliavin calculus and related topics, 2006.

B. [. Rubino and . Tuffin, Rare Event Simulation using Monte-Carlo Methods, 2009.
DOI : 10.1002/9780470745403

URL : https://hal.archives-ouvertes.fr/hal-00787654

S. [. Rockafellar and . Uryasev, Optimization of conditional value-at-risk, The Journal of Risk, vol.2, issue.3, pp.21-42, 2000.
DOI : 10.21314/JOR.2000.038

]. E. Sim16 and . Simonnet, Combinatorial analysis of the adaptive last particle method, Statistics and Computing, vol.26, issue.12, pp.211-230, 2016.

D. Straub, I. Papaioannou, and W. Betz, Bayesian analysis of rare events, Journal of Computational Physics, vol.314, pp.538-556, 2016.
DOI : 10.1016/j.jcp.2016.03.018