The most important characteristics of the investment projects in the energy sector are large sunk investment costs (irreversibility), flexibility of investment timing, and uncertainty related to the existence of commodity derivatives.
In that context, the main goal of an investor in the energy sector is to determine the right time to invest, expand, switch, or abandon the investment decision. Unfortunately, the traditional investment evaluation methods are no longer sufficient to deal properly with risk and uncertainty but real options analysis can successfully deal with this specific characteristic of investments in light of irreversibility, managerial and flexibility. Real options analysis (ROA) is based on analytical techniques originally developed for financial derivatives pricing and follows the fundamental principle of market value maximization.
This case study is designed to determine the optimal timing to invest (option to invest) in a specific project. The first step is to identify the most suitable type of real option for the project, develop a model, and select an appropriate ROA solution method.
Within the framework of this case study, the investment project value (discounted net cash flow) should be defined first. For this purpose, the technical characteristics of the project (technology), as well as stochastic financial parameters, are applied to the project value simulation (Monte Carlo Simulation). Applying the real options theory from the lecture and additional information from the exercise unit, an adequate ROA solution method (for the defined type of option) is used and programmed in Python. As a result, the decision to invest will be made when the present value of the project for the given discounted cash flow turns out to be higher than the optimal continuation value (deferral).
In this section you will find the presentation with:
- some basics of financial options as well as real options; different options evaluation methods also for power generation assets,
- some more information about two selected real options evaluation methods for power generation asset with examples in Python.
Furthermore, we provide the description of the case study.
