Robust optimal decisions with imprecise forecasts
Gulpinar, Nalan and Rustem, Berc. (2007) Robust optimal decisions with imprecise forecasts. Computational Statistics & Data Analysis, Vol.51 (No.7). pp. 3595-3611. ISSN 0167-9473Full text not available from this repository.
Official URL: http://dx.doi.org/10.1016/j.csda.2006.11.036
A robust minimax approach for optimal investment decisions with imprecise return forecasts and risk estimations in financial portfolio management is considered. Single-period and multi-period mean-variance optimization models are extended to worst-case design with multiple rival risk estimations and return forecasts. In multi-period stochastic formulation of classical mean-variance portfolio optimization problem, an investor makes an investment decision based on expectations and/or scenarios up to some intermediate times prior to the horizon and, consequently, rebalances or restructures the portfolio. Multi-period portfolio optimization entails the construction of a scenario tree representing a discretized estimate of uncertainties and associated probabilities in future stages. It is well known that return forecasts and risk estimations are inherently inaccurate and there are different rival estimates, or scenario trees. Robust optimization models are presented and imprecise nature of moment forecasts to reduce the risk of making a decision based on the wrong scenario is addressed. The worst-case performance is guaranteed in view of all rival risk and return scenarios and will only improve when any scenario other than the worst-case is realized. The ex-ante performance of minimax models is tested using historical data and backtesting results are presented. (c) 2006 Elsevier B.V. All rights reserved.
|Item Type:||Journal Article|
|Subjects:||Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Q Science > QA Mathematics
|Divisions:||Faculty of Social Sciences > Warwick Business School|
|Journal or Publication Title:||Computational Statistics & Data Analysis|
|Publisher:||Elsevier Science Ltd|
|Date:||1 April 2007|
|Number of Pages:||17|
|Page Range:||pp. 3595-3611|
|Access rights to Published version:||Restricted or Subscription Access|
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