Article ID: | iaor20043150 |
Country: | United Kingdom |
Volume: | 31 |
Issue: | 1 |
Start Page Number: | 1 |
End Page Number: | 20 |
Publication Date: | Jan 2004 |
Journal: | Computers and Operations Research |
Authors: | Mulvey John M., Shetty Bala |
Keywords: | risk, programming: probabilistic |
This paper describes a framework for modeling significant financial planning problems based on multi-stage optimization under uncertainty. Applications include risk management for institutions, banks, government entities, pension plans, and insurance companies. The approach also applies to individual investors who are interested in integrating investment choices with savings and borrowing strategies. A dynamic discrete-time structure addresses realistic financial issues. The resulting stochastic program is enormous by current computer standards, but it possesses a special structure that lends itself to parallel and distributed optimization algorithms. Interior-point methods are particularly attractive. Solving these stochastic programs presents a major challenge for the computational research and computer science community.