Worst-case robust decisions for multi-period mean-variance portfolio optimization
Gulpinar, Nalan and Rustem, Berc. (2007) Worst-case robust decisions for multi-period mean-variance portfolio optimization. European Journal of Operational Research, Vol.183 (No.3). pp. 981-1000. ISSN 0377-2217Full text not available from this repository.
Official URL: http://dx.doi.org/10.1016/j.ejor.2006.02.046
In this paper, we extend the multi-period mean-variance optimization framework to worst-case design with multiple rival return and risk scenarios. Our approach involves a min-max algorithm and a multi-period mean-variance optimization framework for the stochastic aspects of the scenario tree. Multi-period portfolio optimization entails the construction of a scenario tree representing a discretised estimate of uncertainties and associated probabilities in future stages. The expected value of the portfolio return is maximized simultaneously with the minimization of its variance. There are two sources of further uncertainty that might require a strengthening of the robustness of the decision. The first is that some rival uncertainty scenarios may be too critical to consider in terms of probabilities. The second is that the return variance estimate is usually inaccurate and there are different rival estimates, or scenarios. In either case, the best decision has the additional property that, in terms of risk and return, performance is guaranteed in view of all the rival scenarios. The ex-ante performance of min-max models is tested using historical data and backtesting results are presented.
|Item Type:||Journal Article|
|Subjects:||H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management|
|Divisions:||Faculty of Social Sciences > Warwick Business School > Operational Research & Management Sciences|
|Journal or Publication Title:||European Journal of Operational Research|
|Publisher:||Elsevier Science BV|
|Date:||16 December 2007|
|Number of Pages:||20|
|Page Range:||pp. 981-1000|
|Access rights to Published version:||Restricted or Subscription Access|
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