Beschreibung:
Asset Liability Management (ALM) is an essential risk management technique in Quantitative Finance and Actuarial Science. It aims to maximise a risk-taker's ability to fulfil future liabilities. ALM is especially critical in environments of elevated interest rate changes, as has been experienced globally between 2021 and 2023. Traditional ALM implementation is still heavily dependent on the judgement of professionals such as Quants, Actuaries or Investment Managers. This over-reliance on human input critically limits ALM performance due to restricted automation, human irrationality and restricted scope for multi-objective optimisation. This paper addressed these limitations by applying Deep Reinforcement Learning (DRL), which optimises through trial, and error and continuous feedback from the environment. We defined the Reinforcement Learning (RL) components to optimise for ALM. These include the RL Agent, Environment, Actions, States and Reward Functions. The results demonstrate that implementing DRL provides a superior approach compared to traditional ALM. DRL allows for increased automation, flexibility, and multi-objective optimisation in ALM, reducing the negative impact of human limitations and improving risk management outcomes. The study shows that DRL can be used both at a given time point and dynamically on a high-frequency basis. DRL achieves similar levels of general duration matching and significantly better dynamic duration matching compared to traditional approaches, with fewer theoretical assumptions and restrictions. The findings and principles presented in this study apply to various institutional risk-takers, including insurers, banks, pension funds, and asset managers. Overall, the application of DRL in ALM provides a promising avenue for improving risk management outcomes