Abstract
Markowitz’s classical mean-variance approach for portfolio selection considers only single-period investments. It has, therefore, received very little attention in the context of long-term investment planning. Nevertheless, considering dynamic aspects, already Markowitz [12] mentioned the attractiveness of multi-period portfolio selection problems for portfolio readjustments during the planning horizon. The direct application of the mean-variance model to multi-stage portfolio problems, however, causes many difficulties. A number of studies have tackled such difficulties, providing suggestions on how the dynamic aspects of portfolio optimization should be considered. One of these suggestions is a reallocation methodology that is based on scenario analysis and a tree approach [14]. In this paper, we apply this methodology to power generation assets, in order to capture the continuously changing values of the economic as well as technical parameters considered when evaluating investments in power plants.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bar-Lev, D., Katz, S.: A portfolio approach to fossil fuel procurement in the electric utility industry. J. Finance. 31(3), 933–947 (1976)
Bazilian, M., Roques, F. (eds.): Analytical methods for energy diversity and security: a tribute to Shimon Awerbuch. Elsevier, Amsterdam (2008)
Elton, E., Gruber, M.: On the optimality of some multi-period portfolio selection criteria. J. Bus. 47(2), 231–243 (1974)
Frauendorfer, K., Siede, H.: Portfolio selection using multistage stochastic programming. CEJOR 7(4), 277–289 (1999)
Glensk B., Madlener R.: Dynamic portfolio selection methods for power generation assets. FCN Working Paper No.16/2011, RWTH Aachen University, November, (2011).
Gülpinar, N., Rustem, B.: Worst-case decisions for multi-period mean-variance portfolio optimization. Eur. J. Oper. Res. 183(3), 981–1000 (2007)
Kleindorfer, P., Li, L.: Multi-Period VaR-Constrained Portfolio Optimization with Application to the Electric Power Sector. Energy J. 26(1), 1–25 (2005)
Korhonen, A.: A dynamic bank portfolio planning model with multiple scenarios, multiple goals and changing priorities. Eur. J. Oper. Res. 30(1), 13–23 (1987)
Li, D., Hg, W.: Optimal dynamic portfolio selection: Multi-period mean-variance formulation. Math. Finance 3(10), 387–406 (2000)
Madlener, R.: Portfolio Optimization of Power Generation Assets. In: Rebennack, S., Pardalos, P.M., Pereira, M.V.F., Iliadis, N.A., Zheng, Q.P. (eds.) Handbook of CO\(_2\) in power systems, pp. 275–296. Springer, Berlin (2012)
Maranas, C., Andrulakis, I., Floudas, C., Berger, A., Mulvey, J.: Solving long-term financial planning problems via global optimization. J. Econ. Dyn. Control 21(7–8), 1405–1425 (1997)
Markowitz, H.: Portfolio Selection: Efficient Diversification of Investments. Blackwell, Cambridge MA and Oxford UK (1959)
Mossin, J.: Optimal multi-period portfolio policies. J. Bus. 41(2), 215–229 (1968)
Mulvey, J., Rosenbaum, D., Shetty, B.: Strategic financial management and operations research. Eur. J. Oper. Res. 97(1), 1–16 (1997)
Östermark, R.: Vector forecasting and dynamic portfolio selection: empirical efficiency of recursive multi-period strategies. Eur. J. Oper. Res. 55(1), 46–56 (1991)
Pereira, M., Pinto, L.: Multi-stage stochastic optimization applied to energy planning. Math. Program. 52(2), 359–379 (1991)
Pereira, M., Pinto, L.: Stochastic optimization of multi-reservoir hydroelectric system: A decomposition approach. Water Resour. Res. 21(6), 779–792 (1985)
Steinbach, M.: Recursive direct algorithms for multistage stochastic programs in financial engineering, pp. 98–23. Konrad-Zuse-Zentrum fur Informationstechnik Berlin, Preprint SC (1998)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Glensk, B., Madlener, R. (2014). Dynamic Portfolio Optimization for Power Generation Assets. In: Helber, S., et al. Operations Research Proceedings 2012. Operations Research Proceedings. Springer, Cham. https://doi.org/10.1007/978-3-319-00795-3_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-00795-3_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-00794-6
Online ISBN: 978-3-319-00795-3
eBook Packages: Business and EconomicsBusiness and Management (R0)