Asset liability modelling and pension schemes: the application of robust optimization to USS

Platanakis, E. and Sutcliffe, C. ORCID: https://orcid.org/0000-0003-0187-487X (2017) Asset liability modelling and pension schemes: the application of robust optimization to USS. European Journal of Finance, 23 (4). pp. 324-352. ISSN 1466-4364 doi: 10.1080/1351847X.2015.1071714

Abstract/Summary

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 the first ALM model of a pension scheme to maximise the Sharpe ratio. We disaggregate pension liabilities into three components - active members, deferred members and pensioners, and transform the optimal asset allocation into the scheme’s projected contribution rate. The robust optimization model is extended to include liabilities and used to derive optimal investment policies for the Universities Superannuation Scheme (USS), benchmarked against the Sharpe and Tint, Bayes-Stein, and Black-Litterman models as well as the actual USS investment decisions. Over a 144 month out-of-sample period robust optimization is superior to the four benchmarks across 20 performance criteria, and has a remarkably stable asset allocation – essentially fix-mix. These conclusions are supported by six robustness checks.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/40783
Identification Number/DOI	10.1080/1351847X.2015.1071714
Refereed	Yes
Divisions	Henley Business School > Finance and Accounting
Uncontrolled Keywords	Robust Optimization; Pension Scheme; Asset-Liability Model; Sharpe Ratio; Sharpe-Tint; Bayes-Stein; Black-Litterman
Publisher	Taylor and Francis
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