Well-posedness and tamed Euler schemes for McKean-Vlasov equations driven by L\'evy noise

Abstract. We prove the well-posedness of solutions to McKean–Vlasov stochastic differential equations driven by Lévy noise under mild assumptions where, in particular, the Lévy measure is not required to be finite. The drift, diffusion and jump coefficients are allowed to be random, can grow super-linearly in the state variable, and all may depend on the marginal law of the solution process. We provide a propagation of chaos result under more relaxed conditions than those existing in the literature, and consistent with our well-posedness result. We propose a tamed Euler scheme for the associated interacting particle system and prove that the rate of its strong convergence is arbitrarily close to 1/2. As a by-product, we also obtain the corresponding results on well-posedness, propagation of chaos and strong convergence of the tamed Euler scheme for McKean–Vlasov stochastic delay differential equations (SDDE) and McKean–Vlasov stochastic differential equations with Markovian switching (SDEwMS), both driven by Lévy noise. Furthermore, our results on tamed Euler schemes are new even for ordinary SDEs driven by Lévy noise and with super-linearly growing coefficients.

[1]  Mean-field games with controlled jumps , 2017 .

[2]  Zhongqiang Zhang,et al.  A Fundamental Mean-Square Convergence Theorem for SDEs with Locally Lipschitz Coefficients and Its Applications , 2012, SIAM J. Numer. Anal..

[3]  Wolf-Jürgen Beyn,et al.  Stochastic C-Stability and B-Consistency of Explicit and Implicit Milstein-Type Schemes , 2015, J. Sci. Comput..

[4]  Chaman Kumar,et al.  On Explicit Approximations for Lévy Driven SDEs with Super-linear Diffusion Coefficients , 2016, 1611.03417.

[5]  Chaman Kumar,et al.  On Milstein approximations with varying coefficients: the case of super-linear diffusion coefficients , 2016, BIT Numerical Mathematics.

[6]  Xiaojie Wang,et al.  The tamed Milstein method for commutative stochastic differential equations with non-globally Lipschitz continuous coefficients , 2011, 1102.0662.

[7]  Mireille Bossy,et al.  A stochastic particle method for the McKean-Vlasov and the Burgers equation , 1997, Math. Comput..

[8]  Antonio Politi,et al.  Ubiquity of collective irregular dynamics in balanced networks of spiking neurons , 2017, bioRxiv.

[9]  F. Delarue,et al.  Global solvability of a networked integrate-and-fire model of McKean–Vlasov type , 2012, 1211.0299.

[10]  Xiaojie Wang,et al.  Mean-square approximations of Lévy noise driven SDEs with super-linearly growing diffusion and jump coefficients , 2018, Discrete & Continuous Dynamical Systems - B.

[11]  G. Yin,et al.  On laws of large numbers for systems with mean-field interactions and Markovian switching , 2019, Stochastic Processes and their Applications.

[12]  Xuerong Mao,et al.  The truncated Euler-Maruyama method for stochastic differential equations , 2015, J. Comput. Appl. Math..

[13]  François Delarue,et al.  Probabilistic Theory of Mean Field Games with Applications I: Mean Field FBSDEs, Control, and Games , 2018 .

[14]  Jiandong Wang,et al.  Maximum principle for delayed stochastic mean-field control problem with state constraint , 2019, Advances in Difference Equations.

[15]  C. Kumar Milstein-type Schemes of SDE Driven by L\'evy Noise with Super-linear Diffusion Coefficients , 2017, 1707.02343.

[16]  Carl Graham,et al.  McKean-Vlasov Ito-Skorohod equations, and nonlinear diffusions with discrete jump sets , 1992 .

[17]  S. Pagliarani,et al.  A Fourier-based Picard-iteration approach for a class of McKean–Vlasov SDEs with Lévy jumps , 2018, Stochastics.

[18]  Ji-Heon Park,et al.  Strong Convergence of Euler Approximations of Stochastic Differential Equations with Delay under Local Lipschitz Condition , 2015 .

[19]  Christoph Reisinger,et al.  First order convergence of Milstein schemes for McKean equations and interacting particle systems , 2020, ArXiv.

[20]  M. Hutzenthaler,et al.  Numerical Approximations of Stochastic Differential Equations With Non-globally Lipschitz Continuous Coefficients , 2012, 1203.5809.

[21]  Chaman Kumar,et al.  On tamed milstein schemes of SDEs driven by Lévy noise , 2014, 1407.5347.

[22]  Christoph Reisinger,et al.  Milstein schemes for delay McKean equations and interacting particle systems , 2020, ArXiv.

[23]  B. Jourdain,et al.  Nonlinear SDEs driven by L\'evy processes and related PDEs , 2007, 0707.2723.

[24]  G. dos Reis,et al.  Simulation of McKean–Vlasov SDEs with super-linear growth , 2018, IMA Journal of Numerical Analysis.

[25]  Michael B. Giles,et al.  Adaptive Euler–Maruyama method for SDEs with nonglobally Lipschitz drift , 2020 .

[26]  Konstantinos Dareiotis,et al.  On Tamed Euler Approximations of SDEs Driven by Lévy Noise with Applications to Delay Equations , 2014, SIAM J. Numer. Anal..

[27]  Markus Fischer,et al.  McKean–Vlasov limit for interacting systems with simultaneous jumps , 2017, Stochastic Analysis and Applications.

[28]  H. McKean,et al.  A CLASS OF MARKOV PROCESSES ASSOCIATED WITH NONLINEAR PARABOLIC EQUATIONS , 1966, Proceedings of the National Academy of Sciences of the United States of America.

[29]  Jean-Pierre Fouque,et al.  Deep Learning Methods for Mean Field Control Problems With Delay , 2019, Frontiers in Applied Mathematics and Statistics.

[30]  Michael Scheutzow,et al.  Propagation of chaos for stochastic spatially structured neuronal networks with delay driven by jump diffusions , 2018, The Annals of Applied Probability.

[31]  S. Sabanis Euler approximations with varying coefficients: The case of superlinearly growing diffusion coefficients , 2013, 1308.1796.

[32]  Chaman Kumar,et al.  On Explicit Tamed Milstein-type scheme for Stochastic Differential Equation with Markovian Switching , 2019, J. Comput. Appl. Math..

[33]  Neelima,et al.  On Explicit Milstein-type Scheme for Mckean-Vlasov Stochastic Differential Equations with Super-linear Drift Coefficient , 2020, Electronic Journal of Probability.

[34]  S. Sabanis A note on tamed Euler approximations , 2013, 1303.5504.

[35]  István Gyöngy,et al.  On stochastic equations with respect to semimartingales I. , 1980 .

[36]  Christoph Reisinger,et al.  An adaptive Euler-Maruyama scheme for McKean SDEs with super-linear growth and application to the mean-field FitzHugh-Nagumo model , 2020, J. Comput. Appl. Math..

[37]  P. Kloeden,et al.  Strong convergence of an explicit numerical method for SDEs with nonglobally Lipschitz continuous coefficients , 2010, 1010.3756.

[38]  T. Faniran Numerical Solution of Stochastic Differential Equations , 2015 .

[39]  Deborah Monique,et al.  Authors' addresses , 2004 .

[40]  Stefan Heinrich,et al.  Multilevel Monte Carlo Methods , 2001, LSSC.

[41]  C'onall Kelly,et al.  Adaptive Euler methods for stochastic systems with non-globally Lipschitz coefficients , 2018, Numer. Algorithms.