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arXiv · 2401.04667

Polynomial rates via deconvolution for nonparametric estimation in McKean-Vlasov SDEs

Chiara Amorino·Denis Belomestny·Vytautė Pilipauskaitė·Mark Podolskij·Shi-Yuan Zhou
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Abstract

This paper investigates the estimation of the interaction function for a class of McKean-Vlasov stochastic differential equations. The estimation is based on observations of the associated particle system at time $T$, considering the scenario where both the time horizon $T$ and the number of particles $N$ tend to infinity. Our proposed method recovers polynomial rates of convergence for the resulting estimator. This is achieved under the assumption of exponentially decaying tails for the interaction function. Additionally, we conduct a thorough analysis of the transform of the associated invariant density as a complex function, providing essential insights for our main results.

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Chiara Amorino, Denis Belomestny, Vytautė Pilipauskaitė, Mark Podolskij, Shi-Yuan Zhou. 2024-01-09. Polynomial rates via deconvolution for nonparametric estimation in McKean-Vlasov SDEs. https://arxiv.org/abs/2401.04667

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