A Framework for Treating Model Uncertainty in the Asset Liability Management Problem

ArXiv ID: 2310.11987 “View on arXiv”

Authors: Unknown

Abstract

The problem of asset liability management (ALM) is a classic problem of the financial mathematics and of great interest for the banking institutions and insurance companies. Several formulations of this problem under various model settings have been studied under the Mean-Variance (MV) principle perspective. In this paper, the ALM problem is revisited under the context of model uncertainty in the one-stage framework. In practice, uncertainty issues appear to several aspects of the problem, e.g. liability process characteristics, market conditions, inflation rates, inside information effects, etc. A framework relying on the notion of the Wasserstein barycenter is presented which is able to treat robustly this type of ambiguities by appropriate handling the various information sources (models) and appropriately reformulating the relevant decision making problem. The proposed framework can be applied to a number of different model settings leading to the selection of investment portfolios that remain robust to the various uncertainties appearing in the market. The paper is concluded with a numerical experiment for a static version of the ALM problem, employing standard modelling approaches, illustrating the capabilities of the proposed method with very satisfactory results in retrieving the true optimal strategy even in high noise cases.

Keywords: Asset Liability Management, Wasserstein barycenter, model uncertainty, robust optimization, Mean-Variance

Complexity vs Empirical Score

• Math Complexity: 9.5/10
• Empirical Rigor: 3.0/10
• Quadrant: Lab Rats
• Why: The paper employs advanced mathematics, including stochastic differential equations, filtration theory, and Wasserstein barycenters for model uncertainty, indicating high mathematical complexity. However, the empirical section relies on a simple static numerical experiment with standard models and lacks detailed implementation, code, or backtesting data, resulting in low empirical rigor.

  flowchart TD
    A["Research Goal<br>Treat model uncertainty<br>in ALM problem"] --> B["Methodology<br>Wasserstein Barycenter Framework"]
    B --> C["Inputs<br>Multiple financial models<br>Liability & asset data"]
    C --> D["Computation<br>Robust optimization<br>reformulation"]
    D --> E{"Outcome"}
    E --> F["Findings: Robust portfolio<br>True optimal strategy recovered<br>High noise tolerance"]