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Capital Budgeting Under Uncertainty---An Integrated Approach Using Contingent Claims Analysis and Integer Programming

Author

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  • Helga Meier

    (Institute of Computer Science, University of Bonn, D-53117, Bonn, Germany)

  • Nicos Christofides

    (Centre for Quantitative Finance, Imperial College, London SW7 2PG, United Kingdom)

  • Gerry Salkin

    (Centre for Quantitative Finance, Imperial College, London SW7 2PG, United Kingdom)

Abstract

Recently the application of contingent claims analysis and dynamic programming to project evaluation has attracted a lot of attention. These techniques are required, for example, if the value of a project develops stochastically over time and the decision to invest into this project can be postponed. Yet, so far there are no considerations regarding how this perception of projects reflects on a capital budgeting situation. We propose two approaches that integrate these methods with traditional capital budgeting models. A simple capital budgeting model can be formulated as the problem of finding the portfolio of options that has maximal value and fulfils the capital expenditure constraint. However, this model has some shortcomings regarding its applicability in traditional budgeting situations. Therefore, we define an alternative optimisation model that uses scenarios to depict a set of possible future states. The optimal portfolio is then equivalent to a dynamic investment strategy that determines a number of state-dependent optimal portfolios.

Suggested Citation

  • Helga Meier & Nicos Christofides & Gerry Salkin, 2001. "Capital Budgeting Under Uncertainty---An Integrated Approach Using Contingent Claims Analysis and Integer Programming," Operations Research, INFORMS, vol. 49(2), pages 196-206, April.
    • Handle: RePEc:inm:oropre:v:49:y:2001:i:2:p:196-206
    DOI: 10.1287/opre.49.2.196.13531
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    Cited by:

    1. Bunn, Derek W. & Oliveira, Fernando S., 2016. "Dynamic capacity planning using strategic slack valuation," European Journal of Operational Research, Elsevier, vol. 253(1), pages 40-50.
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    3. Marchioni, Andrea & Magni, Carlo Alberto, 2018. "Investment decisions and sensitivity analysis: NPV-consistency of rates of return," European Journal of Operational Research, Elsevier, vol. 268(1), pages 361-372.
    4. Anabela Costa & José Paixão, 2010. "An approximate solution approach for a scenario-based capital budgeting model," Computational Management Science, Springer, vol. 7(3), pages 337-353, July.
    5. Onofre Martorell Cunill & Carles Mulet Forteza & Micaela Rosselló Miralles, 2008. "Valuing Growth Strategy Management by Hotel Chains Based on the Real Options Approach," Tourism Economics, , vol. 14(3), pages 511-526, September.
    6. Wilbaut, Christophe & Salhi, Saïd & Hanafi, Saïd, 2009. "An iterative variable-based fixation heuristic for the 0-1 multidimensional knapsack problem," European Journal of Operational Research, Elsevier, vol. 199(2), pages 339-348, December.
    7. S H Martzoukos, 2009. "Real R&D options and optimal activation of two-dimensional random controls," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(6), pages 843-858, June.
    8. Janne Kettunen & Derek W. Bunn & William Blyth, 2011. "Investment Propensities under Carbon Policy Uncertainty," The Energy Journal, , vol. 32(1), pages 77-118, January.
    9. Arnaud Fréville & SaÏd Hanafi, 2005. "The Multidimensional 0-1 Knapsack Problem—Bounds and Computational Aspects," Annals of Operations Research, Springer, vol. 139(1), pages 195-227, October.
    10. Janne Kettunen, Derek W. Bunn and William Blyth & Derek W. Bunn & William Blyth, 2011. "Investment Propensities under Carbon Policy Uncertainty," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 77-118.
    11. Gerald G. Brown & Robert F. Dell & Alexandra M. Newman, 2004. "Optimizing Military Capital Planning," Interfaces, INFORMS, vol. 34(6), pages 415-425, December.
    12. Patrizia Beraldi & Maria Bruni & Antonio Violi, 2012. "Capital rationing problems under uncertainty and risk," Computational Optimization and Applications, Springer, vol. 51(3), pages 1375-1396, April.
    13. Magni, Carlo Alberto & Marchioni, Andrea & Baschieri, Davide, 2023. "The Attribution Matrix and the joint use of Finite Change Sensitivity Index and Residual Income for value-based performance measurement," European Journal of Operational Research, Elsevier, vol. 306(2), pages 872-892.
    14. S. Leitner & D.A. Behrens, 2015. "On the efficiency of hurdle rate-based coordination mechanisms," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 21(5), pages 413-431, September.
    15. Juan Pineiro-Chousa & Marcos Vizcaíno-González, 2020. "A mathematical model for the role of third party funding in reputation building of academic institutions," Review of Managerial Science, Springer, vol. 14(2), pages 365-377, April.
    16. Freville, Arnaud, 2004. "The multidimensional 0-1 knapsack problem: An overview," European Journal of Operational Research, Elsevier, vol. 155(1), pages 1-21, May.
    17. Pineiro-Chousa, Juan & Vizcaíno-González, Marcos, 2016. "A quantum derivation of a reputational risk premium," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 304-309.
    18. Chan, Yupo & DiSalvo, Joseph P. & Garrambone, Michael W., 2005. "A goal-seeking approach to capital budgeting," Socio-Economic Planning Sciences, Elsevier, vol. 39(2), pages 165-182, June.
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