Abstract:The revenue risk is considerable in infrastructure project financing arrangements such as build-operate-transfer ͑BOT͒. A potential mitigation strategy for the revenue risk is a governmental revenue guarantee, where the government secures a minimum amount of revenue for a project. Such a guarantee is: ͑1͒ only redeemable at distinct points in time; and ͑2͒ more economical if the government limits the guarantee's availability to the early portions of a BOT's concession period. Hence, a guarantee characterized by this type of structure takes the form of either a Bermudan or a simple multiple-exercise real option, depending upon the number of exercise opportunities afforded. The multi-least-squares Monte Carlo technique is presented and illustrated as a promising approach to determine the fair value of this variety of real option. This method is far more flexible than prevailing approaches, so it represents an important step toward improving risk mitigation and facilitating contractual and financial negotiations in BOT projects.
Countries around the world have welcomed Public Private Partnerships (PPPs) as an alternative to finance infrastructure. For strategic projects with high demand uncertainty, a government may decide to provide a concessionaire with a Minimum Revenue Guarantee (MRG) to mitigate revenue risk and to help enhance the project's credit, thereby reducing the financing costs of the project. However, government revenue guarantees can pose fiscal risks to the issuing government if too many significant claims are redeemed at the same time. This undesirable circumstance can be exacerbated during an economic recession in which tax revenues are low and the costs of subsidies are potentially higher than expected. This paper presents a new model of government revenue guarantees by which revenue guarantee thresholds are adjusted over time to reflect the inter-temporal risk profiles of the project. Revenue risk is modeled using a stochastic process called the Variance Model. Then, revenue shortfalls and revenue excesses are modeled as multi-early exercise options, and priced using multi-least squares Monte Carlo method. Finally, an illustrative example of a Build-Operate-Transfer (BOT) highway project demonstrates how the proposed model may be applied in practice at the project evaluation stage. The proposed model may help to promote fairer risk allocation between the host government and the concessionaire.
Assessment of BOT project financial risk is generally performed by combining Monte Carlo simulation with discounted cash flow analysis. The outcomes of this risk assessment depend, to a significant extent, upon the total project uncertainty, which aggregates aleatory and epistemic uncertainties of key risk variables. Unlike aleatory uncertainty, modelling epistemic uncertainty is a rather difficult endeavour. In fact, BOT epistemic uncertainty may vary according to the significant information disclosed during the concession period. Two properties generally characterize the stochastic behaviour of the uncertainty of BOT epistemic variables: (1) the learning property and (2) the increasing uncertainty property. A new family of Markovian processes, the Martingale variance model and the general variance model, are proposed as an alternative modelling tool for BOT risk variables. Unlike current stochastic models, the proposed models can be adapted to incorporate a risk analyst's view of properties (1) and (2). A case study, a hypothetical BOT transportation project, illustrates that failing to properly model a project's epistemic uncertainty may lead to a biased estimate of the project's financial risk. The variance models may support, guide and extend the thinking process of risk analysts who face the challenging task of representing subjective assessments of key risk factors.Build-operate-transfer, Monte Carlo simulation, risk analysis, stochastic models,
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