The F\"ollmer-Schweizer decomposition under incomplete information

TL;DR

This paper characterizes the F"ollmer-Schweizer decomposition under incomplete information, linking it to projections and filtering in partially observable stochastic models, including jump-diffusions.

Contribution

It provides a general characterization of the decomposition under partial information and explicit computation methods for Markovian and jump-diffusion models.

Findings

Characterization of the integrand in the decomposition under partial information.

Explicit computation methods using filtering for Markovian models.

Finite-dimensional filter-based computation for jump-diffusion models.

Abstract

In this paper we study the F\"ollmer-Schweizer decomposition of a square integrable random variable $\xi$ with respect to a given semimartingale $S$ under restricted information. Thanks to the relationship between this decomposition and that of the projection of $\xi$ with respect to the given information flow, we characterize the integrand appearing in the F\"ollmer-Schweizer decomposition under partial information in the general case where $\xi$ is not necessarily adapted to the available information level. For partially observable Markovian models where the dynamics of $S$ depends on an unobservable stochastic factor $X$, we show how to compute the decomposition by means of filtering problems involving functions defined on an infinite-dimensional space. Moreover, in the case of a partially observed jump-diffusion model where $X$ is described by a pure jump process taking values in a finite dimensional space, we compute explicitly the integrand in the F\"ollmer-Schweizer decomposition by working with finite dimensional filters.