Risk measurement and management of defined benefit pension schemes: a stochastic approach

The traditional actuarial valuation for defined benefit pensionschemes operates on the basis of a set of deterministic calculationscombined with actuarial judgment. It has played an importantrole in guiding decision-making as far as the level of fundingis concerned. The paper argues that stochastic methods can addvalue in certain crucial areas, in particular the financialrisk management of such schemes. The traditional approach torisk is to incorporate margins in the valuation assumptions;however, a stochastic approach allows the user to evaluate specificand quantifiable risk and performance measures in respect ofalternative funding and investment strategies. The paper introducesa framework that measures the risks inherent in asset allocationand contribution rate decisions, allowing decisions to be madeon a more informed basis. In doing this, we suggest and applysome potential risk and performance measures. This frameworkprovides the means to explore the trade-offs involved in possiblecontribution and asset allocation decisions and leads to decisionstrategies that are expected to give improved outcomes for thesame level of risk. A realistic case study is used to illustratethe properties of the methodology and how it might be used.     

Distribution assumptions and risk constraints in portfolio optimization

Empirical distributions are often claimed to be superior to parametric distributions, yet to also increase the computational complexity and are therefore hard to apply in portfolio optimization. In this paper, we approach the portfolio optimization problem under constraints on the portfolios Value at Risk and Expected Tail Loss, respectively, under empirical distributions for the Standard and Poors 100 stocks. We apply a heuristic optimization method which has been found to overcome the restrictions of traditional optimization techniques. Our results indicate that empirical distributions might turn into a Pandoras Box: Though highly reliable for predicting the assets risks, employing these distributions in the optimization process might result in severe mis-estimations of the resulting portfolios actual risk. It is found that even a simple mean-variance approach can be superior despite its known specification errors.AMS Classification: G11, C61Dietmar G. Maringer: Im grateful to two anonymous referees, Peter Winker, Manfred Gilli, Berç Rustem, Erricos Kontoghiorghes, Alfred Lehar, Josef Zechner, Suresh Sundaresan, and conference participants at Aix-en-Provence, Limassol, and Sydney for valuable discussions and comments on earlier versions of this paper.