Robust optimal asset-liability management under square-root factor processes and model ambiguity: a BSDE approach

Abstract

This article studies robust optimal asset-liability management problems for an ambiguity-averse manager in a possibly non-Markovian environment with stochastic investment opportunities. The manager has access to one risk-free asset and one risky asset in a financial market. The market price of risk relies on a stochastic factor process satisfying an affine-form, square-root, Markovian model, whereas the risky asset’s return rate and volatility are potentially given by general non-Markovian, unbounded stochastic processes. This financial framework includes, but is not limited to, the constant elasticity of variance (CEV) model, the family of 4/2 stochastic volatility models, and some path-dependent non-Markovian models, as exceptional cases. As opposed to most of the papers using the Hamilton-Jacobi-Bellman-Issacs (HJBI) equation to deal with model ambiguity in the Markovian cases, we address the non-Markovian case by proposing a backward stochastic differential equation (BSDE) approach. By solving the associated BSDEs explicitly, we derive, in closed form, the robust optimal controls and robust optimal value functions for power and exponential utility, respectively. In addition, analytical solutions to some particular cases of our model are provided. Finally, the effects of model ambiguity and market parameters on the robust optimal investment strategies are illustrated under the CEV model and 4/2 model with numerical examples.

KEYWORDS:

• Ambiguity aversion
• asset-liability management
• backward stochastic differential equation
• non-Markovian model
• square-root factor process

Acknowledgements

The authors are grateful to Prof. Jesper Lund Pedersen, the editors, and two anonymous reviewers for their constructive comments and suggestions, which greatly improve the quality of this article.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research receives partial financial support from the University of Copenhagen.