Crafting asset allocation for a re-insurer via portfolio optimisation
Date
2022-02-01
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Abstract
One of the most challenging tasks faced by financial advisors and consultants, relates to the phenomena of portfolio selection. This process typically entails selecting asset classes based on their risk and reward attributes. Striking an optimal balance between risk and reward is no easy task, given its conflicting nature. This phenomena is referred to as portfolio optimisation and is commonly formulated and solved via the well known mean-variance optimisation procedure, based on the pioneering works by Harry Markowitz. The objective function is formulated as a quadratic programming problem, that seeks to maximise expected return whilst minimising risk. While this approach presents an auspicious foundation to solve a portfolio optimisation problem, it does not incorporate the unique liabilities (such as future payments or claims) inherent to
most institutional investors.
The aim of the study is therefore to provide a roadmap outlining how assets and liabilities are dovetailed to enhance the decision making process around portfolio optimisation. To achieve this, the notion and premise of asset-liability management (ALM) and liability-driven investing (LDI) are introduced to better manage both assets and liabilities, coherently. This would ultimately ensure an institutional investor's long term financial sustainability. To add a practical ingredient to this thesis, a real-world case study for a re-insurer is examined. Essentially, the roadmap is applied to a case study to solve a complete portfolio optimisation problem, from an LDI perspective.
The results of the unconstrained asset allocation reveal the optimiser's preference to allocate chiefly to a small range of asset classes. While this outcome may be theoretically appropriate, this presents a practical challenge given potential concentration risks, and lack of portfolio diversification opportunities. For this reason, constraints are imposed within the optimisation procedure, resulting in a more diversified and larger array of asset classes to include within a portfolio. To aid with the model validation component and to serve as credence, subject matter experts are consulted. The outcome of this validation was that the process embarked upon as well as the results produced are reasonable and resonates with industry standards. To supplement the model validation and to serve as a reasonability check, a comprehensive sensitivity analysis was undertaken on key input parameters such as expected return to assess the impact this has on the optimal portfolio of assets.
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Keywords
Liability driven investing (LDI), Asset allocation, Asset liability management (ALM), Portfolio optimisation, Road-map