Portfolio optimization under model uncertainty and BSDE games

We consider robust optimal portfolio problems for markets modeled by (possibly non-Markovian) Itô-Lévy processes. Mathematically, the situation can be described as a stochastic differential game, where one of the players (the agent) is trying to find the portfolio that maximizes the utility of her t...

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Bibliographic Details
Published in:Quantitative finance 2011-11, Vol.11 (11), p.1665-1674
Main Authors: Øksendal, Bernt, Sulem, Agnès
Format: Article
Language:English
Subjects:
BSDEs
Differential equations
Exponential utility
G11, C70
Game theory
Games
Itô-Lévy processes
Markov analysis
Mathematical models
Model uncertainty
Portfolio management
Portfolio optimization
Stochastic differential games
Studies
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Summary:We consider robust optimal portfolio problems for markets modeled by (possibly non-Markovian) Itô-Lévy processes. Mathematically, the situation can be described as a stochastic differential game, where one of the players (the agent) is trying to find the portfolio that maximizes the utility of her terminal wealth, while the other player ("the market") is controlling some of the unknown parameters of the market (e.g., the underlying probability measure, representing a model uncertainty problem) and is trying to minimize this maximal utility of the agent. This leads to a worst case scenario control problem for the agent. In the Markovian case, such problems can be studied using the Hamilton-Jacobi-Bellman-Isaacs (HJBI) equation, but these methods do not work in the non-Markovian case. We approach the problem by transforming it into a stochastic differential game for backward stochastic differential equations (a BSDE game). Using comparison theorems for BSDEs with jumps we arrive at criteria for the solution of such games in the form of a kind of non-Markovian analogue of the HJBI equation. The results are illustrated by examples.
ISSN:1469-7688
1469-7696
DOI:10.1080/14697688.2011.615219