2010
DOI: 10.4007/annals.2010.171.2115
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Densities for rough differential equations under Hörmander’s condition

Abstract: Abstract. We consider stochastic differential equations dY = V (Y ) dX driven by a multidimensional Gaussian process X in the rough path sense. Using Malliavin Calculus we show that Yt admits a density for t ∈ (0, T ] provided (i) the vector fields V = (V 1 , ..., V d ) satisfy Hörmander's condition and (ii) the Gaussian driving signal X satisfies certain conditions. Examples of driving signals include fractional Brownian motion with Hurst parameter H > 1/4, the Brownian Bridge returning to zero after time T a… Show more

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Cited by 120 publications
(137 citation statements)
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“…(Remark that we could have considered perturbation h ∈ H β for some β < 0, which en passant shows that the effective tangent space to gPAM is larger than the Cameron-Martin space. 3 …”
Section: Introductionmentioning
confidence: 99%
“…(Remark that we could have considered perturbation h ∈ H β for some β < 0, which en passant shows that the effective tangent space to gPAM is larger than the Cameron-Martin space. 3 …”
Section: Introductionmentioning
confidence: 99%
“…(This was a crucial ingredient in the development of Malliavin calculus for rough differential equations driven by fBm, see e.g. [CF10,CHLT15]. )…”
Section: Introductionmentioning
confidence: 99%
“…This theory allows one to solve (deterministically) differential equations driven by rough signals at the expense of "enhancing" the rough signal with some additional information. Lyons' theory has found numerous applications to stochastic calculus and stochastic differential equations, for example see [4], [5], [6], [8], and the references therein. For some more recent applications, see [1], [19], [18], [9] , and [2].…”
Section: Introductionmentioning
confidence: 99%