Nicola Chiara

Variance models for project financial risk analysis with applications to greenfield BOT highway projects

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 analysts view of properties (1) and (2). A case study, a hypothetical BOT transportation project, illustrates that failing to properly model a projects epistemic uncertainty may lead to a biased estimate of the projects 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.

Using real options for revenue risk mitigation in transportation project financing

Effective risk management is essential for success in transportation project financing arrangements such as build–operate–transfer (BOT). Both sponsors and lenders consider the revenue risk an extremely important factor when they assess a BOT projects feasibility. One potential strategy for mitigating the revenue risk is a revenue guarantee, in which a guarantor secures a minimum amount of revenue for a project; such guarantees take the form of a put option. However, the inclusion of such guarantees in BOT arrangements is hampered by the lack of methods to determine the value, or the fair price, of these types of options. Current valuation techniques lack the flexibility to structure the options in a manner that is affordable to the government and attractive to the private sector. This significant shortcoming opens a research opportunity to explore the development of methods for valuing more flexible and affordable guarantee structures. This paper presents two new valuation methods, the multi–least squares Monte Carlo method and the multi-exercise boundary method, which model the revenue guarantee as a multiple-exercise real option. The two valuation methods successfully combine Monte Carlo simulation and dynamic programming techniques to price multiple-exercise real options. A hypothetical case study illustrates the application and the potential of the two methods to serve as tools for risk mitigation in BOT projects.

A strategic partnering framework analysis methodology for public‐private partnerships

Purpose – The purpose of this paper is to view and analyse public‐private partnerships (PPPs) under a strategic partnering approach between the key parties involved, i.e. public sector, private sector and lenders, and their business environment.Design/methodology/approach – A strategic partnering framework analysis methodology has been devised based on existing and well‐known business strategic analysis tools (the political‐economic‐social‐technological (PEST) and strengths‐weaknesses‐opportunities‐threats (SWOT) analysis). The methodology consists of modules and may be used to identify the potential of strategic partnering in a sector and/or country and/or for a particular project in a procurement process. By using appropriate modules of the methodology, public sector partnering requirements or the market potential for PPPs, in general, may be assessed.Findings – The small‐scale application of a module of the methodology is demonstrated through an international consultation on the influence of the presen...

A modeling government revenue guarantees in privately built transportation projects: a risk-adjusted approach

Abstract 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 projects 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 exc...

Alternative to Government Revenue Guarantees: Dynamic Revenue Insurance Contracts

AbstractPublic private partnerships (PPPs) are arrangements under which the private sector supplies infrastructure assets and services that traditionally have been provided by the public sector. Public authorities may enhance the marketability of PPP projects by offering revenue guarantees. However, government revenue guarantees can pose significant fiscal risks for the issuing government, particularly during economic crises. This paper presents a new type of revenue risk hedging contract, the dynamic (flexible) revenue insurance contract, which can be offered as an alternative to the conventional government guarantees. This new contract gives PPP stakeholders other than the government the opportunity to participate in the revenue risk coverage. Potential revenue risk insurers include international financial institutions, export credit agencies, and private insurance companies. The key feature of these new contracts is that they facilitate the pooling of project revenue insurers by accommodating insurer f...

Improving Economic Efficiency of Public-Private Partnerships for Infrastructure Development by Contractual Flexibility Analysis in a Highly Uncertain Context

Public-private partnerships (PPPs), as long-term contractual relationships between the public and private sector, are usually controlled by a rigid contractual structure. This principle can reduce transaction costs but sacrifice opportunities to make PPPs more economically efficient by allocating and addressing future downside risks appropriately and flexibly during a long-term concession, which is full of unpredictable uncertainties that cause the failure of many infrastructure development projects under PPPs procurement. This article aims to present a novel type of proactive uncertainty management, contractual flexibility analysis (CFA), which can improve the economic efficiency of PPPs by incorporating flexibilities into the current way of contract structuring.

Modelling completion risk using stochastic critical path-envelope method: a BOT highway project application

In integrated project delivery methods such as build‐operate‐transfer (BOT), a thorough financial risk analysis model should incorporate completion risk analysis into operation risk analysis as the timing of financial events such as refinancing and debt servicing depend on the construction completion date. During construction, project managers always have opportunities to react to negative events and to take corrective actions whenever possible to recover late‐running schedules. These opportunities to react are ‘real options’ embedded in the construction process. However, current models of completion risk analysis ignore this feature of project managers. A reliable construction completion risk model for project feasibility studies should capture a managers option to react to unforeseen, negative events. A novel approach for modelling construction completion risk analysis is developed by combining stochastic critical path method with the envelope method (SCP‐EM). The SCP‐EM approach can model the option‐like feature of management feedback reactions in a straightforward fashion. The proposed approach, if applied correctly during the project feasibility study stage, enhances the project finance risk model by helping analysts properly evaluate financial risk arising from completion delay.

Sample Disturbance in Resonant Column Test Measurement of Small-Strain Shear-Wave Velocity

The accurate assessment of dynamic soil properties is a crucial step in the solution process of geotechnical earthquake engineering problems. The resonant column test is one of the ordinary procedures for dynamic characterization of soil. In this paper, the impact of sample disturbance on the resonant column test measurement of small-strain S-wave velocity is examined. Sample disturbance is shown to be a function of the ratio of the laboratory to field S-wave velocities: Vs, lab/Vs,field. The influence of four parameters - soil stiffness, soil plasticity index, in-situ sample depth and in-situ effective mean confining pressure - on sample disturbance is investigated both qualitatively and quantitatively. The relative importance of each parameter in predicting the small-strain field S-wave velocity from the resonant column test values is illustrated and predictive equations are presented.

Stochastic Optimization of Capital Structure in PrivatelyFunded Infrastructure Projects

Capital structure optimization is a key aspect to ensure the success of infrastructure financing. Interest in capital structure optimization in infrastructure projects has been growing rapidly because of the prevalence of public-private partnerships in the U.S. and private finance initiative in the United Kingdom. Even though it is recognized that there are three types of financial sources (i.e., equity, mezzanine, and debt capital) in funding an infrastructure project, the traditional capital structure optimization method either did not consider the existence of mezzanine finance or treat it to be debt or equity instruments. This assumption is not valid in the new era when more and more inflows of capital into infrastructure development projects are from all kinds of institutional investors and multilateral development finance institutions through private equity-style funds. These new equity investors are willing to take advantage of mezzanine financial instruments and common shares as a vehicle to invest in infrastructure assets, which makes a huge difference with traditional equity providers. The frequent implementation of convertible security as one kind of mezzanine financial instrument makes the prediction of the evolution of capital structure impossible due to the dynamic stopping time of the contingent claim embedded in convertible security. Thus, the traditional method for capital structure optimization in the new era is not tenable any more. The principal objective of this article is to present a new model from the perspective of project promoters, which considers convertible security in infrastructure financing and identifies optimal mix of equity, debt, mezzanine capital by incorporating stochastic dynamic programming into the traditional approach.

Copula-Based Portfolio Credit Risk Assessment in Infrastructure Project Financing

Project lenders are concerned about credit risk management in infrastructure project financing. According to the maxim in risk management, “you cannot manage what you cannot measure,” so project lenders want to know the exposure of their debt loss. Although many sophisticated credit risk models have been developed for corporate financing, only some relevant literature has focused on credit risk assessment for infrastructure projects. However, the models developed assume that project debt lenders invest only in one single project at a time. In fact, project lenders are inclined to be simultaneously involved in a portfolio of assets around the world rather than a single local project asset in order to diversify away idiosyncratic risks of an individual infrastructure project. Consequently, in order to meet project lenders’ needs, this article presents a copula-based model to measure the credit risk of a portfolio of projects by implementing the variance model and the double stochastic intensity model. A numerical example of two interdependent projects is shown as a case study to illustrate how to quantify this joint default risk in infrastructure project financing.