Asset and Liability Management in Insurance Firms: A Biased-Randomised Approach Combining Heuristics with Monte-Carlo Simulation

Abstract

The management of assets and liabilities is of critical importance for insurance companies and banks. Complex decisions need to be made regarding how to assign assets to liabilities such in a way that the overall benefit is maximised over a multi-period horizon. At the same time, the risk of not being able to cover the liabilities at any given period must be kept under a certain threshold level. This optimisation problem is known in the literature as the asset and liability management (ALM) problem. In this work, we propose a biased-randomised algorithm to solve a real-life instance of the ALM problem. Firstly, we outline a greedy heuristic. Secondly, we transform it into a probabilistic algorithm by employing Monte-Carlo simulation and biased-randomisation techniques. According to our computational tests, the probabilistic algorithm is able to generate, in short computing times, solutions that outperform by far the ones currently practised in the sector.