Estimating net present value variability for deterministic models

For decision makers the variability in the net present value (NPV) of an investment project is an indication of the project's risk. So-called risk analysis is one way to estimate this variability. However, risk analysis requires knowledge about the stochastic character of the inputs. For large, longterm investment projects (such as energy infrastructures) modelling their stochastic character is often difficult, if not impossible. The analysis of the variability is then restricted to deterministic sensitivity analysis, such as one factor at a time and scenario analysis. However, these deterministic analyses do not account for the total variability in the NPV. It will be shown that the use of experimental design, taken from statistical theory, in combination with regression metamodelling is a better approach to estimating the variability of the NPV.