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Considering there is a lack of research in determining the optimal levels of government guarantee and revenue cap, the objective of this research is to determine their optimal levels to achieve a reasonable financial risk allocation between governments and private investors while avoiding overly lucrative conditions for private investors.

Expanded net present value (NPV) analysis and bargaining game theory are employed to construct the core of the determination process. The risk gap between governments and private investors is assessed via an expanded NPV analysis to see if the financial risk has been shared reasonably, based on which the range of the government guarantee is decided. A bargaining model is then created to help locate the optimal level of the government guarantee. Finally, a revenue cap, often combined with the government guarantee in public–private partnership (PPP) agreements, will be determined if overly lucrative conditions for private investors are observed or governments suffer a risk spillover.

Referring to a real PPP project in Australia, Project BA is created to validate the applicability of the proposed determination process. The outcome shows that the proposed determination process in this paper is capable of determining the optimal levels of government guarantee and revenue cap. The government preferences towards risk allocation will influence the values of the optimal levels. Governments may also consider to alleviate the control over investors' net profits to mobilise private investors into PPP projects.

There is a potential possibility that the revenue cap fails to control the financial risk for governments or the overly lucrative condition for private investors. In other words, even though the revenue cap is set at the minimal level, the financial risk for governments still beyond their tolerance range or the overly lucrative condition for private investors still occurs. Future research may focus on other financial protective schemes which help to better control the financial risks for governments and profits for private investors.

Government guarantees are frequently used as an investment incentive to reduce the probabilities of suffering loss for private investors. Nevertheless, the financial risks for governments may increase after providing guarantees and, as a result, revenue cap is required by governments to avoid placing themselves in an unprotected situation. By recognising the importance of the two contractual parameters, many scholars dig into their option values. However, there are very rare research works focussing on the method of determining the specific levels of government guarantee and revenue cap. To overcome the limitations of existing models and enrich the methodology for government guarantee and revenue cap determination, this paper contributes to the body of knowledge by developing a government guarantee and revenue cap determination process which contributes to a reasonable allocation of financial risks between governments and private investors.

Targeting public–private partnership (PPP) rental retirement villages, the purpose of this paper is to bring forward the solution of insufficient research in a non-competitive guarantee (a restrictive agreement) towards the compensation and guarantee costs in consideration of benefit redistribution if the governments are unable to keep the promise on guarantee provision.

Real option principles are applied to assess the public–private investment proportions and the expected return rates of the private sector in a non-competitive guarantee and analyse their effects on the public–private benefit and risk allocations as well as the success of the project. Instead of granting direct capital support, this research accomplishes the compensation of non-competition guarantee by adjusting the project benefit distribution ratios between the government and the private sector to achieve the option value of the guarantee. An empirical example with alternative scales, which is developed from an existing rental village in Geelong, is used to numerically verify the research process.

The results illustrate that the option value of the non-competition guarantee plays an important role in supporting the implementation of the PPP rental retirement village projects. The option value of the non-competition guarantee has a close relationship with the guarantee level and the government guarantee cost, which is positively correlated with the guarantee level and negatively correlated with the government guarantee cost. To reduce the government guarantee cost, the government should carefully determine the public–private investment proportion, appropriately control the return rate of the private sector and approve the construction of the new project after the investment recovery of the private sector.

This research mainly focusses on the economic loss of the government due to the guarantee responsibility. Further research could be conducted to determine the guarantee level more precisely and take the social cost of the government guarantees into consideration.

This research is the first attempt to investigate the government compensation and costs of non-competition guarantee for PPP rental retirement village projects and will enhance the understanding of the nature of PPP applications. The evaluation process and the implementation of the compensation through the adjustment of benefit distribution provides a comprehensive method to analyse the non-competition guarantee of PPP projects and help the parties negotiate in good faith to agree on a method of redress.

In this paper, we study the role of government financial guarantees as catalyst for project finance (PF). On the one hand, the government's incentive compatibility and participation constraint determine the optimal portion of the loan to be backed. On the other, the borrowing interest rate satisfies the debtholders’ participation constraint. The project's sponsor may choose to underinvest or overinvest depending on its own capital contribution, the risk technology, the risk measurement errors, and the proportion of guarantee provided by the government. We derive the project optimal investment level as well as the government partial loan guarantee coverage. We also discuss the impact of the risk measurement errors on the project's credit spreads.

Host governments often provide guarantees in build‐operate‐transfer (BOT) infrastructure projects to attract private sector investors. Problems arise because the governments often do not know the full extent of contingent liabilities when issuing guarantees, and because they account and record guarantee costs only when guarantees come due. This paper discusses the guarantees' financial impact from the perspectives of the government and the project sponsor. A typical Indonesian BOT toll road project is taken as the case study. Stochastic simulation using Latin Hypercube technique is applied on the cash flow model with and without guarantees. Several types of guarantees including minimum revenue guarantee, maximum interest rate guarantee, debt guarantee, tariff guarantee and minimum traffic guarantee are discussed. Simulation results reveal that guarantees can reduce risk but are not free of cost. If compared with equivalent subsidies, however, some guarantees can be more effective in lessening the extent of project risk.

The purpose of this paper is to deal with the maximum interest rate guarantees (MIRGs), and develop a methodology for setting the optimal value of the interest rate cap, namely the maximum interest rate above which the private investor will obtain reimbursement from the government, which balances the interests of the parties involved in the project.

The mechanism underlying the MIRG is modeled through real options. Monte Carlo simulation is employed as the option-pricing method. The resulting real option-based model is applied to the case of the “Camionale di Bari” toll road (Southern Italy).

The application provides some insights for the policy maker called to define the proper forms of guarantees. Furthermore, the results support the negotiation process, allowing the different actors to structure the guarantee in a way that satisfies all the parties and fairly allocates risks between them according to different operational and financial conditions.

The novelty of the contribution is triple. First, the authors advance the state of the art on government supports by focusing on the interest rate guarantee. Second, the authors enrich the existing studies on MIRG by proposing a quantitative model to set the guarantee in compliance with the public–private win-win principle. The developed real option-based model supports the decision maker in finding the optimal value of the interest rate cap, which is able to satisfy the interests of the parties involved in the project. Third, the authors consider not only the private sponsor and the government, as traditionally made by the models developed for other guarantees, but also the lender.

This paper investigates the accounting and reporting of government interventions during the most recent global financial crisis. It shows that governments do not report all their interventions as required by accounting standards. The incompleteness of information may systematically lead to erroneous decisions and therefore jeopardize financial sustainability. Particularly relevant are shortcomings in the field of consolidation and the presentation of financial guarantees.

PPPs can impose important future public costs, while PPP government guarantees create explicit contingent liabilities similar to public debt obligations. The risk that arises from such partnerships must be transparently valued to assess a country's fiscal profile. The purpose of this study is to show that the notion of a PPP as a (set of) contingent claim(s) can also be used to value the PPP public risk.

Taking a finance perspective, the paper refers to more traditional cases of asset valuation, applies them to a PPP and compares more carefully different set-ups of a PPP. The paper introduces and analyzes the different scenarios that were at the government's disposal for executing a transport infrastructure project.

The findings reveal that, for the first years, the burden on the surplus or deficit will be less in the case of the PPP compares to typical public investment. Secondly, the net contingent PPP flows constitute the real effect on the deficit and correspondingly on the public debt and weaken the government's fiscal position. Finally, the paper attributes a specific price to the PPP public risk introducing CDS valuation with and without counterparty (government) default.

This study, by proposing a method to evaluate PPP risk, complements previous literature on PPPs, which touches upon issues mainly related to the description of PPP types, the effect of contingent commitments and the accounting classification of PPP assets.

The Build‐Operate‐Transfer (BOT) model of development of privatized infrastructure projects is implemented through the award of a concession to a private sector consortium which will finance, build and operate the facility. In a competitive BOT tender, the selection of the successful consortium does not depend on the lowest tolls offered by the tenderer. Rather, it is dependent on the ability of the promoter to provide the most competitive package of distinctive winning elements in its proposal during the final negotiations. The promoter must fully understand the government's needs and concerns and be able to address them through the right package of the winning elements. In this paper, these elements are developed from sub‐factors of the critical success factors of technical solution advantage, financial package differentiation and differentiation in guarantees.

Post-disaster housing reconstruction (PDHR) demands a considerable percentage of global property investment, yet the post-disaster environment presents intricate challenges to reconstruction financing for governments and at the same time, revenue uncertainty for private investors. The purpose of this study is to develop a methodology for tackling land shortage and the financial challenges of PDHR in the aftermath of a disaster.

This study developed a methodology based on a combined minimum revenue guarantee and maximum revenue cap model using a well-established real options analysis (ROA) for revenue risk sharing in PDHR projects and land readjustment (LR) for finance. The applicability of the purported model is demonstrated through an illustrative example.

The results show that flexibility in the options could increase the PDHR contractor’s risk profile by increasing the expected value of the contractor investment and reducing the probability of investment loss. On the other side, a cap on the contractor revenue stream would allow the government to benefit from any excess in revenue and would counterbalance the value of the option.

The framework proposed in this study could serve as a practical risk-revenue sharing in PDHR projects. Governments and policymakers could use the findings to enable the successful delivery of PDHR projects and consequently bring the quality of life of affected people to pre-disaster conditions.

This study can be considered as a first attempt toward the use of the Australian barrier style options structure, and the trinomial lattice valuation model in PDHR projects, which incorporates LR, public-private partnerships, governmental guarantees and PDHR concepts in one ROA-based framework.

This paper aims to examine the impact of government financial assistance provided to Australian small and medium-sized enterprises (SMEs).

This study uses firm-level panel data on more than 2,000 SMEs over a five-year period from the Business Longitudinal Database compiled by the Australian Bureau of Statistics. The authors measure the impact of government financial assistance in terms of subsequent SME performance (income from sales of goods and services and profitability) and changes in the availability of alternative nongovernment finance.

The authors find government financial assistance helps SMEs improve performance over and above the effects of conventional financing. They also find than the implicit guarantee effect signalled by a firm receiving government financial assistance suggests firms are more likely to obtain nongovernment finance in the future. Control factors that significantly affect SME performance and finance availability include business size, the level of innovation, business objectives and industry.

Nearly all of the responses in the original survey data are qualitative, so we are unable to assess how the strength of these relationships varies by the levels of assistance, income and profitability. The measure of government financial assistance of the authors is also general in that it includes grants, subsidies and rebates from any Australian Government organisation, so we are unable to comment on the impact of individual federal, state or local government programmes.

Government financial assistance helps SMEs improve both immediate and future performance as measured by income and profitability. This could be because government financial assistance quickly overcomes the financial constraints endemic in SMEs. Government financial assistance also helps SMEs obtain nongovernment finance in the future. The authors conjecture that this is because it overcomes some of the information opaqueness of SMEs.

Few studies focus on the impact of direct government financial assistance compared with indirect assistance as typical in credit guarantee schemes. The authors use a very large and detailed data set on Australian SMEs to undertake the analysis.