Article, 2023

Robust optimal asset-liability management with mispricing and stochastic factor market dynamics

Insurance Mathematics and Economics, ISSN 0167-6687, Volume 113, Pages 251-273, 10.1016/j.insmatheco.2023.09.001

Contributors

Wang N. [1] Zhang Y. 0000-0003-0915-5116 (Corresponding author) [2]

Affiliations

  1. [1] Macquarie Business School
  2. [NORA names: Australia; Oceania; OECD];
  3. [2] University of Copenhagen
  4. [NORA names: KU University of Copenhagen; University; Denmark; Europe, EU; Nordic; OECD]

Abstract

This paper investigates a robust optimal asset-liability management problem under an expected utility maximization criterion. More specifically, the manager is concerned about the potential model uncertainty and aims to seek the robust optimal investment strategies. We incorporate an uncontrollable random liability described by a generalized drifted Brownian motion. Also, the manager has access to an incomplete financial market consisting of a risk-free asset, a market index with potentially path-dependent, time-varying risk premium and volatility, and a pair of mispriced stocks. The market dynamics are assumed to rely on an affine-form, square-root factor process and the price error is modeled by a co-integrated system. We adopt a backward stochastic differential equation approach hinging on the martingale optimality principle to solve this non-Markovian robust control problem. Closed-form expressions for the robust optimal investment strategies, the probability perturbation process under the well-defined worst-case scenario and the corresponding value function are derived. The admissibility of the robust optimal controls is verified under some technical conditions. Finally, we perform some numerical examples to illustrate the effects of model parameters on the robust investment strategies and draw some economic interpretations from these results.

Keywords

Asset-liability management, Backward stochastic differential equation, Mispricing, Model ambiguity, Stochastic factor

Funders

  • Higher Education Discipline Innovation Project
  • National Natural Science Foundation of China

Data Provider: Elsevier