Asset Liability Modelling and Pension Schemes: The Application of Robust Optimization to USS

This paper uses a novel numerical optimization technique - robust optimization - that is well suited to solving the asset-liability management (ALM) problem for pension schemes. It requires the estimation of fewer stochastic parameters, reduces estimation risk and adopts a prudent approach to asset allocation. This study is the first to apply it to a real-world pension scheme and to use the Sharpe ratio as the objective of an ALM problem. We also disaggregate the pension liabilities into three components - active members, deferred members and pensioners, and transform the optimal asset allocation into the scheme’s projected contribution rate. We extend the robust optimization model to include liabilities, and use it to derive optimal investment policies for the Universities Superannuation Scheme (USS), benchmarked against the same ALM model with deterministic parameters, the Sharpe and Tint model and the actual USS investment decisions. Over a 144 month out-of-sample period we find that robust optimization is superior to the three benchmarks on all the performance criteria.

Keywords: robust optimization, pension scheme, asset-liability model, Sharpe ratio, Sharpe-Tint model