Name
Abstract | ||
|---|---|---|
In this paper we develop a robust model for portfolio optimization. The purpose is to consider parameter uncertainty by controlling the impact of estimation errors on the portfolio strategy performance. We construct a simple robust mean absolute deviation (RMAD) model which leads to a linear program and reduces computational complexity of existing robust portfolio optimization methods. This paper tests the robust strategies on real market data and discusses performance of the robust optimization model empirically based on financial elasticity, standard deviation, and market condition such as growth, steady state, and decline in trend. Our study shows that the proposed robust optimization generally outperforms a nominal mean absolute deviation model. We also suggest precautions against use of robust optimization under certain circumstances. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.cor.2010.10.020 | Computers & OR |
Keywords | DocType | Volume |
Risk,Robust optimization,robust mean absolute deviation,portfolio strategy performance,robust portfolio optimization method,Linear programming,robust optimization model empirically,proposed robust optimization,robust optimization,Investment,nominal mean absolute deviation,robust strategy,robust model,portfolio optimization | Journal | 38 |
Issue | ISSN | Citations |
9 | Computers and Operations Research | 11 |
PageRank | References | Authors |
0.59 | 14 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yongma Moon | 1 | 29 | 2.78 |
| Tao Yao | 2 | 93 | 8.93 |