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2021 On explicit Milstein-type scheme for McKean–Vlasov stochastic differential equations with super-linear drift coefficient
Chaman Kumar, Neelima
Author Affiliations +
Electron. J. Probab. 26: 1-32 (2021). DOI: 10.1214/21-EJP676

Abstract

We introduce an explicit Milstein-type scheme for McKean–Vlasov stochastic differential equations using the notion of a measure derivative given by P.-L. Lions in his lectures at the Collège de France and presented in [9]. We show that the proposed Milstein-type scheme converges to the true solution in strong sense with a rate equal to 1.0. The drift coefficient is allowed to grow super-linearly in the state variable and both the drift and the diffusion coefficients are assumed to be only once differentiable in variables corresponding to state and measure. Furthermore, derivatives of drift and diffusion coefficients with respect to the measure component are uniformly bounded. The challenges arising due to the dependence of coefficients on measures are tackled and our findings are consistent with the analogous results for stochastic differential equations.

Funding Statement

The first named author gratefully acknowledges the financial support provided by Science and Engineering Research Board (SERB) under its MATRICS program through grant number SER-1329-MTD.

Acknowledgments

We are thankful to the anonymous referees for several insightful comments and suggestions which improved the presentation of the manuscript.

Citation

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Chaman Kumar. Neelima. "On explicit Milstein-type scheme for McKean–Vlasov stochastic differential equations with super-linear drift coefficient." Electron. J. Probab. 26 1 - 32, 2021. https://doi.org/10.1214/21-EJP676

Information

Received: 25 June 2020; Accepted: 7 July 2021; Published: 2021
First available in Project Euclid: 15 August 2021

Digital Object Identifier: 10.1214/21-EJP676

Subjects:
Primary: 60H35 , 65C05 , 65C30 , 65C35

Keywords: explicit Milstein scheme , McKean–Vlasov SDE , propagation of chaos , rate of strong convergence , super-linear coefficient

Vol.26 • 2021
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