Smoothness of the density for solutions to Gaussian rough differential equations
arXiv:1209.3100 · doi:10.1214/13-AOP896
Abstract
We consider stochastic differential equations of the form driven by a multi-dimensional Gaussian process. Under the assumption that the vector fields and satisfy Hörmander's bracket condition, we demonstrate that admits a smooth density for any , provided the driving noise satisfies certain nondegeneracy assumptions. Our analysis relies on relies on an interplay of rough path theory, Malliavin calculus and the theory of Gaussian processes. Our result applies to a broad range of examples including fractional Brownian motion with Hurst parameter , the Ornstein-Uhlenbeck process and the Brownian bridge returning after time .
Published in at http://dx.doi.org/10.1214/13-AOP896 the Annals of Probability (http://www.imstat.org/aop/) by the Institute of Mathematical Statistics (http://www.imstat.org)
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- Constrained Rough Paths
- A flow-based approach to rough differential equations
- Path-by-path regularisation through multiplicative noise in rough, Young, and ordinary differential equations
- The Hörmander condition for delayed stochastic differential equations
- Penalisation techniques for one-dimensional reflected rough differential equations
- A support and density theorem for Markovian rough paths
- Precise Local Estimates for Hypoelliptic Differential Equations driven by Fractional Brownian Motions
- Regularity of the Ito-Lyons map
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- On the Variational Regularity of Cameron-Martin paths
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- A Version of Hörmander's Theorem for Markovian Rough Paths
- A Stratonovich-Skorohod integral formula for Volterra Gaussian rough paths
- Smoothness of densities for path-dependent SDEs under Hörmander's condition
- Optimal convergence rate of modified Milstein scheme for SDEs with rough fractional diffusions