Risk minimization in financial markets modeled by It\^o-L\'evy processes

TL;DR

This paper surveys recent methods for risk minimization in financial markets modeled by Itô-Lévy processes, introduces new results on stochastic maximum principles, and explores risk measures, portfolio optimization, and recursive utility.

Contribution

It provides new results on the stochastic maximum principle and connects risk measures with portfolio optimization in jump-diffusion models.

Findings

Introduction of dual and BSDE representations of risk measures

Development of a stronger stochastic maximum principle

Explicit examples of risk-minimizing portfolios

Abstract

This paper is mainly a survey of recent research developments regarding methods for risk minimization in financial markets modeled by It\^o-L\'evy processes, but it also contains some new results on the underlying stochastic maximum principle. The concept of a convex risk measure is introduced, and two representations of such measures are given, namely: (i) the dual representation and (ii) the representation by means of backward stochastic differential equations (BSDEs) with jumps. Depending on the representation, the corresponding risk minimal portfolio problem is studied, either in the context of stochastic differential games or optimal control of forward-backward SDEs. The related concept of recursive utility is also introduced, and corresponding recursive utility maximization problems are studied. In either case the maximum principle for optimal stochastic control plays a crucial role, and in the paper we prove a version of this principle which is stronger than what was previously known. The theory is illustrated by examples, showing explicitly the risk minimizing portfolio in some cases.