Generalized covariation and extended Fukushima decompositions for Banach valued processes. Application to windows of Dirichlet processes

preprint2011arXivOpen access

Abstract

This paper concerns a class of Banach valued processes which have finite quadratic variation. The notion introduced here generalizes the classical one, of Métivier and Pellaumail which is quite restrictive. We make use of the notion of χχ-covariation which is a generalized notion of covariation for processes with values in two Banach spaces B1B_{1} and B2B_{2}. χχ refers to a suitable subspace of the dual of the projective tensor product of B1B_{1} and B2B_{2}. We investigate some C1C^{1} type transformations for various classes of stochastic processes admitting a χχ-quadratic variation and related properties. If $\X^1$ and $\X^2$ admit a χχ-covariation, Fi:Bi→RF^i: B_i \rightarrow \R, i=1,2i = 1, 2 are of class C1C^1 with some supplementary assumptions then the covariation of the real processes $F^1(\X^1)$ and $F^2(\X^2)$ exist. A detailed analysis will be devoted to the so-called window processes. Let XX be a real continuous process; the C([−τ,0])C([-τ,0])-valued process X(⋅)X(\cdot) defined by Xt(y)=Xt+yX_t(y) = X_{t+y}, where y∈[−τ,0]y \in [-τ,0], is called {\it window} process. Special attention is given to transformations of window processes associated with Dirichlet and weak Dirichlet processes. In fact we aim to generalize the following properties valid for B=RB=\R. If $\X=X$ is a real valued Dirichlet process and F:B→RF:B \rightarrow \R of class C1(B)C^{1}(B) then $F(\X)$ is still a Dirichlet process. If $\X=X$ is a weak Dirichlet process with finite quadratic variation, and F:C0,1([0,T]×B)F: C^{0,1}([0,T]\times B) is of class C0,1C^{0,1}, then $[ F(t, \X_t) ] $ is a weak Dirichlet process. We specify corresponding results when B=C([−τ,0])B=C([-τ,0]) and $\X=X(\cdot)$. This will consitute a significant Fukushima decomposition for functionals of windows of (weak) Dirichlet processes. As applications, we give a new technique for representing path-dependent random variables.

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