yes / 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 maximize 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.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/8146 |
| Date | 05 August 2015 |
| Creators | Platanakis, Emmanouil, Sutcliffe, C. |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Article, Author's final draft |
| Rights | © 2015 Taylor & Francis. This is an Author's Original Manuscript of an article published by Taylor & Francis in European Journal of Finance, 2015 available online at http://dx.doi.org/10.1080/1351847X.2015.1071714 |
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