English

Fractional smoothness of functionals of diffusion processes under a change of measure

Probability 2012-10-18 v1 Analysis of PDEs Functional Analysis

Abstract

Let v:[0,T]×RdRv:[0,T]\times \R^d \to \R be the solution of the parabolic backward equation \partial_t v + (1/2) \sum_{i,l} [\sigma \sigma^\perp]_{il} \partial_{x_i \partial_{x_l} v + \sum_{i} b_i \partial_{x_i}v + kv =0 with terminal condition gg, where the coefficients are time- and state-dependent, and satisfy certain regularity assumptions. Let X=(Xt)t[0,T]X=(X_t)_{t\in [0,T]} be the associated Rd\R^d-valued diffusion process on some appropriate (Ω,\cF,\Q)(\Omega,\cF,\Q). For p[2,)p\in [2,\infty) and a measure d=λTd\Qd\P=\lambda_T d\Q, where λT\lambda_T satisfies the Muckenhoupt condition AαA_\alpha for α(1,p)\alpha \in (1,p), we relate the behavior of g(XT)\eptg(XT)Lp()\|g(X_T)-\ept g(X_T) \|_{L_p(\P)}, v(t,Xt)Lp()\|\nabla v(t,X_t) \|_{L_p(\P)} and D2v(t,Xt)Lp()\|D^2 v(t,X_t) \|_{L_p(\P)} to each other, where D^2v:=(\partial_{x_i \partial_{x_l}v)_{i,l} is the Hessian matrix.

Keywords

Cite

@article{arxiv.1210.4572,
  title  = {Fractional smoothness of functionals of diffusion processes under a change of measure},
  author = {Stefan Geiss and Emmanuel Gobet},
  journal= {arXiv preprint arXiv:1210.4572},
  year   = {2012}
}