Approximating Random Variables by Stochastic Integrals

Schweizer, Martin

Ann. Probab., Tome 22 (1994) no. 4, p. 1536-1575 / Harvested from Project Euclid

Résumé

Let $X$ be a semimartingale and $\Theta$ the space of all predictable $X$-integrable processes $\vartheta$ such that $\int\vartheta dX$ is in the space $\mathscr{S}^2$ of semimartingales. We consider the problem of approximating a given random variable $H \in\mathscr{L}^2$ by a stochastic integral $\int^T_0 \vartheta_s dX_s$, with respect to the $\mathscr{L}^2$-norm. If $X$ is special and has the form $X = X_0 + M + \int \alpha d\langle M\rangle$, we construct a solution in feedback form under the assumptions that $\int \alpha^2 d\langle M\rangle$ is deterministic and that $H$ admits a strong F-S decomposition into a constant, a stochastic integral of $X$ and a martingale part orthogonal to $M$. We provide sufficient conditions for the existence of such a decomposition, and we give several applications to quadratic optimization problems arising in financial mathematics.

Détail

Publié le : 1994-07-14
Classification:  Semimartingales,  stochastic integrals,  strong F-S decomposition,  mean-variance tradeoff,  option pricing,  financial mathematics,  60G48,  60H05,  90A09