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On the Estimates of the Density of Feynman-Kac Semigroups of $alpha$-Stable-like Processes

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 Added by Chunlin Wang
 Publication date 2006
  fields
and research's language is English
 Authors Chunlin Wang




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Suppose that $alpha in (0,2)$ and that $X$ is an $alpha$-stable-like process on $R^d$. Let $F$ be a function on $R^d$ belonging to the class $bf{J_{d,alpha}}$ (see Introduction) and $A_{t}^{F}$ be $sum_{s le t}F(X_{s-},X_{s}), t> 0$, a discontinuous additive functional of $X$. With neither $F$ nor $X$ being symmetric, under certain conditions, we show that the Feynman-Kac semigroup ${S_{t}^{F}:t ge 0}$ defined by $$ S_{t}^{F}f(x)=mathbb{E}_{x}(e^{-A_{t}^{F}}f(X_{t}))$$ has a density $q$ and that there exist positive constants $C_1,C_2,C_3$ and $C_4$ such that $$C_{1}e^{-C_{2}t}t^{-frac{d}{alpha}}(1 wedge frac{t^{frac{1}{alpha}}}{|x-y|})^{d+alpha} leq q(t,x,y) leq C_{3}e^{C_{4}t}t^{-frac{d}{alpha}}(1 wedge frac{t^{frac{1}{alpha}}}{|x-y|})^{d+alpha}$$ for all $(t,x,y)in (0,infty) times R^d times R^d$.

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46 - Chunlin Wang 2006
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We consider a real-valued diffusion process with a linear jump term driven by a Poisson point process and we assume that the jump amplitudes have a centered density with finite moments. We show upper and lower estimates for the density of the solution in the case that the jump amplitudes follow a Gaussian or Laplacian law. The proof of the lower bound uses a general expression for the density of the solution in terms of the convolution of the density of the continuous part and the jump amplitude density. The upper bound uses an upper tail estimate in terms of the jump amplitude distribution and techniques of the Malliavin calculus in order to bound the density by the tails of the solution. We also extend the lower bounds to the multidimensional case.
260 - Lucas Izydorczyk 2019
This paper presents a partial state of the art about the topic of representation of generalized Fokker-Planck Partial Differential Equations (PDEs) by solutions of McKean Feynman-Kac Equations (MFKEs) that generalize the notion of McKean Stochastic Differential Equations (MSDEs). While MSDEs can be related to non-linear Fokker-Planck PDEs, MFKEs can be related to non-conservative non-linear PDEs. Motivations come from modeling issues but also from numerical approximation issues in computing the solution of a PDE, arising for instance in the context of stochastic control. MFKEs also appear naturally in representing final value problems related to backward Fokker-Planck equations.
We exhibit an exact simulation algorithm for the supremum of a stable process over a finite time interval using dominated coupling from the past (DCFTP). We establish a novel perpetuity equation for the supremum (via the representation of the concave majorants of Levy processes) and apply it to construct a Markov chain in the DCFTP algorithm. We prove that the number of steps taken backwards in time before the coalescence is detected is finite. We analyse numerically the performance of the algorithm (the code, written in Julia 1.0, is available on GitHub).
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