Let $X$ be an $\mathbb{R}^d$-valued continuous semimartingale, $T$ a
fixed time horizon and $\Theta$ the space of all $\mathbb{R}^d$ -valued
predictable $X$ -integrable processes such that the stochastic integral
$G(\vartheta)=\int\vartheta dX$ is a square-integrable semimartingale. A recent
paper gives necessary and sufficient conditions on $X$ for $G_T(\Theta)$ to be
closed in $L^2(P)$. In this paper, we describe the structure of the
$L^2$-projection mapping an $\mathscr{F}_T$-measurable random variable $H \in
L^2(P)$ on $G_T(\theta)$ and provide the resulting integrand $\vartheta^H \in
\Theta$ feedback form. This is related to variance-optimal hedging strategies
in financial mathematics and generalizes previous results imposing very
restrictive assumptions on $X$. Our proofs use the variance-optimal martingale
measure $\tilda{P}$ for $X$ and weighted norm inequalities relating $\tilda{P}$
to the original measure $P$.
@article{1023481112,
author = {Rheinl\"ander, Thorsten and Schweizer, Martin},
title = {On $L^2$-projections on a space of stochastic integrals},
journal = {Ann. Probab.},
volume = {25},
number = {4},
year = {1997},
pages = { 1810-1831},
language = {en},
url = {http://dml.mathdoc.fr/item/1023481112}
}
Rheinländer, Thorsten; Schweizer, Martin. On $L^2$-projections on a space of stochastic integrals. Ann. Probab., Tome 25 (1997) no. 4, pp. 1810-1831. http://gdmltest.u-ga.fr/item/1023481112/