Exponential Bounds for the Density of the Law of the Solution of an SDE with Locally Lipschitz Coefficients

Under the uniform Hörmander hypothesis, we study the smoothness and exponential bounds of the density of the law of the solution of a stochastic differential equation (SDE) with locally Lipschitz drift that satisfies a monotonicity condition. We extend the approach used for SDEs with globally Lipschitz coefficients and obtain estimates for the Malliavin covariance matrix and its inverse. Based on these estimates and using the Malliavin differentiability of any order of the solution of the SDE, we prove exponential bounds of the solution’s density law. These results can be used to study the convergence of implicit numerical schemes for SDEs.

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Publication Details

Journal
Mathematics
Published
2025-02-27
DOI
https://doi.org/10.3390/math13050798
Primary Topic
Stochastic processes and financial applications
Type
article
Field-Weighted Citation Impact
0.00

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article

Exponential Bounds for the Density of the Law of the Solution of an SDE with Locally Lipschitz Coefficients

Cristina Antón
Mathematics
Stochastic processes and financial applications
article

Exponential Bounds for the Density of the Law of the Solution of an SDE with Locally Lipschitz Coefficients

Cristina Antón
article en

Abstract

Under the uniform Hörmander hypothesis, we study the smoothness and exponential bounds of the density of the law of the solution of a stochastic differential equation (SDE) with locally Lipschitz drift that satisfies a monotonicity condition. We extend the approach used for SDEs with globally Lipschitz coefficients and obtain estimates for the Malliavin covariance matrix and its inverse. Based on these estimates and using the Malliavin differentiability of any order of the solution of the SDE, we prove exponential bounds of the solution’s density law. These results can be used to study the convergence of implicit numerical schemes for SDEs.

MathematicsVol. 13(5)
MacEwan University (CA)
Natural Sciences and Engineering Research Council of Canada
Openalex Percentile: Top 95%
Stochastic processes and financial applications
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