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Publisher: Taylor and Francis
Languages: English
Types: Article
Subjects:

Classified by OpenAIRE into

ACM Ref: ComputingMilieux_LEGALASPECTSOFCOMPUTING
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.\ud \ud \ud \ud
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Ang, A., Chen, B. and Sundaresan, S. (2013) Liability-Driven Investment with Downside Risk, Journal of Portfolio Management, vol. 40, no. 1, Fall, pp. 71-87.
    • Bader, L.N. (2003) The Case Against Stock in Corporate Pension Funds, Pension Section News, no. 51, February, pp. 17-19.
    • Ben-Tal, A. and Nemirovski, A. (1998) Robust Convex Optimization, Mathematics of Operations Research, vol. 23, no. 4, pp. 769-805.
    • Ben-Tal, A. and Nemirovski, A. (1999) Robust Solutions of Uncertain Linear Programs, Operations Research Letters, vol. 25, no. 1, pp. 1-13.
    • Ben-Tal, A. and Nemirovski, A. (2000) Robust Solutions of Linear Programming Problems Contaminated with 1 d 
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