A Structural Model of Credit Risk for Illiquid Debt

Blanc-Brude, Frédéric; Hasan, Majid

doi:10.3905/jfi.2016.26.1.006

Cited by 9 publications

(4 citation statements)

References 23 publications

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“…While the results of the estimated parameters of fitted models to DSCR observations suggest the power-law model fitted to the mean values has a higher R 2 value, in the case of half-variance values the fitted linear function has a has a higher R 2 value in contrast to the power-law model. This can be attributed to the observed coefficient of variation (COV) values of the annual DSCR as reported in Blanc-Brude and Hasan (2016).…”

Section: Resultsmentioning

confidence: 81%

“…These data collected manually and verified from the audited statements of accounts of several hundred project companies, as well as DSCR data reported by private contributors. In this paper without loss of generality of the proposed methodology, the applicability of the proposed method is shown on a worked example assuming that the statistics of DSCR follow those of real-life projects gathered by Blanc-Brude and Hasan (2016). It is also has been verified by Blanc-Brude et al (2018) that DSCR follows a Gaussian process.…”

Section: Worked Examplementioning

confidence: 93%

“…These undocumented data, both in industry and academic domain are not available in large datasets. Blanc-Brude and Hasan (2016) collected a large sample of realized DSCR observations consisting of a total of 207 projects spanning more than 15 years from the early 1990s to 2015. These data collected manually and verified from the audited statements of accounts of several hundred project companies, as well as DSCR data reported by private contributors.…”

Section: Worked Examplementioning

confidence: 99%

“…In the case of project finance, where the credit appraisal is based on the project cash flows rather than the general assets or the credit of the sponsor, definition of default based on project FCF is common (Sorge and Gadanecz, 2004; Chowdhury, 2017). For this reason, in this study, it is assumed that material default is triggered whenever the DSCR stochastic process hits for the first time the boundary equal to one (Yescombe, 2014; Blanc-Brude and Hasan, 2016). The stochastic revenue stream of a toll road project is dependent on the long-term dynamics of the demand of the toll road over the concession period of PPP agreement.…”

Section: Introductionmentioning

confidence: 99%

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Risk transfer in project finance loans for toll road using credit default swaps

Wei

Firouzi²,

2022

JFMPC

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Purpose The purpose of this paper is to demonstrate the applicability of the Credit Default Swaps (CDS), as a financial instrument, for transferring of risk in project finance loans. Also, an equation has been derived for pricing of CDS spreads. Design/methodology/approach The debt service cover ratio (DSCR) is modeled as a Brownian Motion (BM) with a power-law model fitted to the mean and half-variance of the existing data set of DSCRs. The survival probability of DSCR is calculated during the operational phase of the project finance deal, using a closed-form analytical method, and the results are verified by Monte Carlo simulation (MCS). Findings It is found that using the power-law model yields higher CDS premiums. This in turn confirms the necessity of conducting rigorous statistical analysis in fitting the best performing model as uninformed reliance on constant time-invariant drift and diffusion model can erroneously result in smaller CDS spreads. A sensitivity analysis also shows that the results are very sensitive to the recovery rate and cost of debt values. Originality/value Insufficiency of free cash flow is a major risk in the toll road project finance and hence there is a need to develop innovative financial instruments for risk management. In this paper, a novel valuation method of CDS is proposed assuming that DSCR follows the BM stochastic process.

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Section: Resultsmentioning

confidence: 81%

Section: Worked Examplementioning

confidence: 93%

Section: Worked Examplementioning

confidence: 99%