Consider a fixed probability space and a Hilbert spaceH; E denotes expectationwith respect toP Intuitively speaking, the Malliavin derivative of a random variableF in Lp is defined by expanding it in terms of Gaussian random variables that are parametrized by the elements of H and differentiating the expansion formally; the Skorokhod integral is the adjoint operation to the Malliavin derivative. Consider a family of R-valued random variables W, indexed by the elements h of the Hilbert spaceH. Assume further that each W is a Gaussian random variable, that the map taking h to W is a linear map, and that the mean and covariancestructure is given by for all g and h in H. It can be shown that, given H, there always exists a probability space and a family of random variables with the above properties. The Malliavin derivative is essentially defined by formally setting the derivative of the random variable W to be h, and then extending this definition to “smooth enough” random variables. For a random variable F of the form where f : Rn → R is smooth, the Malliavin derivative is defined using the earlier “formal definition” and the chain rule: In other words, whereas F was a real-valued random variable, its derivative DF is an H-valued random variable, an element ofthe spaceLp. Of course, this procedure only defines DF for “smooth” random variables, but an approximation procedure can be employed to define DF for F in a large subspace of Lp; the domain of D is the closure of the smooth random variables in the seminorm : This space is denoted by D1,p and is called the Watanabe–Sobolev space.
The Skorokhod integral
For simplicity, consider now just the case p = 2. The Skorokhod integralδ is defined to be the L2-adjoint of the Malliavin derivative D. Just as D was not defined on the whole of L2, δ is not defined on the whole of L2: the domain of δ consists of those processes u in L2 for which there exists a constantCsuch that, for all F in D1,2, The Skorokhod integral of a process u in L2 is a real-valued random variable δu in L2; if u lies in the domain of δ, then δu is defined by the relation that, for all F ∈ D1,2, Just as the Malliavin derivative D was first defined on simple, smooth random variables, the Skorokhod integral has a simple expression for “simple processes”: if u is given by with Fj smooth and hj in H, then
Properties
The isometry property: for any process u in L2 that lies in the domain of δ,
The derivative of a Skorokhod integral is given by the formula
The Skorokhod integral of the product of a random variable F in D1,2 and a process u in dom is given by the formula