Name
Abstract | ||
|---|---|---|
Recent work by Han and Van Roy [Han J, Van Roy B (2011) Control of diffusions via linear programming. Infanger G, ed. Stochastic Programming: The State of the Art, in Honor of George B. Dantzig (Springer, New York), 329-354] introduced a linear programming technique to compute good suboptimal solutions to high-dimensional control problems in a diffusion-based setting. Their problem formulation worked with finite horizon problems where the horizon, T, is an exponentially distributed random variable. We extend their approach to finite horizon problems with a fixed horizon T. We also apply these techniques to dynamic portfolio optimization problems and then simulate the resulting policies to obtain lower bounds on the optimal value functions. We also use these policies in conjunction with convex duality methods designed for portfolio optimization problems to construct upper bounds on the optimal value functions. In our numerical experiments we find that the primal and dual bounds are very close, and so we conclude, for these problems at least, that the linear programming approach performs very well. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1287/ijoc.2015.0651 | INFORMS JOURNAL ON COMPUTING |
Keywords | Field | DocType |
linear programming, portfolio optimization, duality | Mathematical optimization,Random variable,Horizon,Duality (optimization),Portfolio optimization,Exponential distribution,Linear programming,Finite horizon,Stochastic programming,Mathematics | Journal |
Volume | Issue | ISSN |
27 | 4 | 1091-9856 |
Citations | PageRank | References |
1 | 0.35 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrew Ahn | 1 | 2 | 1.10 |
| Martin Haugh | 2 | 165 | 20.21 |