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

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.

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