Recent research has demonstrated that structural credit risk models are capable of explaining the credit risk process for private, illiquid debt. This article extends this literature by proposing a simple and intuitive calibration approach using Bayesian inference to capture the nonlinear dynamics of debt service cover ratios using a new dataset of private cash flows collected by hand for 267 European infrastructure projects spanning 17 years. The combination of a cash flow–driven structural model with observable cash flow data and Bayesian inference enables the measurement of default risk even when few or no defaults have been or can be observed, whereas reduced-form models like the ones used by rating agencies necessarily lead to biased credit risk estimates for private debt.
In this paper, we extend the structural credit risk model of illiquid debt developed by Blanc-Brude and Hasan (2016) (BBH) to incorporate the step-in option of senior creditors in PF and model its impact on the valuation and risk profile of senior unsecured project debt, taking into account the bargaining power of creditors and borrowers in investment projects that are relationship specific. Step-in options can also be understood as trading-off credit risk and duration, depending on creditor risk preferences.
Large infrastructure projects are often financed through limited-recourse project finance (PF) vehicles with a high proportion of senior debt. PF is a unique form of corporate governance that creates extensive creditor rights when certain covenants are broken, most notably the option to ”step-in” upon a credit event, and to restructure the firm to maximise either expected recovery or expected payoff, depending on the nature of the credit event. These options significantly impact the outcome of credit events, and credit rating agencies report anecdotal evidence of very high recovery rates in project finance debt compared to comparable corporate debt.
However, data paucity forbids robust reduced-form modelling of expected recovery rates. In this paper, we extend the structural credit risk model of illiquid debt developed by Blanc-Brude and Hasan (2016) (BBH) to incorporate the step-in option of senior creditors in PF and model its impact on the valuation and risk profile of senior unsecured project debt, taking into account the bargaining power of creditors and borrowers in investment projects that are relationship specific. Step-in options can also be understood as trading-off credit risk and duration, depending on creditor risk preferences.
In this paper, we develop a structural credit risk model that relies on cash flow data to derive credit risk metrics. The model is useful for illiquid assets for which a time series of prices is not observable.
Our methodology is designed to require a parsimonious dataset of observable inputs, and provides a clear link between an asset’s fundamental characteristics and its risk profile. The model is flexible enough to value debt instruments with path-dependant cash flows, such as mortgages and floating rate loans, and can incorporate various debt covenants, such as debt refinancing, and restructuring options, as well as cash sweeps, dividend lockups, and reserve accounts.
The implementation of the model is illustrated with project finance debt, which is highly illiquid, and suffers from a serious lack of price data. We show that the dynamics of the debt service cover ratio (DSCR) along with the debt repayment profile and the debt covenants is sufficient to implement our credit risk model.
For reasonable parameter values of the DSCR dynamics, the model reproduces stylised empirical regularities regarding the probabilities of default for two generic types of infrastructure projects.
The objectives of this paper are to document the statistical characteristics of debt service cover ratios (DSCRs) in infrastructure project finance, and to develop and calibrate a model of DSCR dynamics.
For this purpose, we collect a large sample of realised DSCR observations across a range of infrastructure projects spanning more than 15 years, representing the largest such sample available for research to date, and conduct a series of statistical tests and analyses to establish the most adequate approach to modelling and predicting future DSCR levels and volatility.
Using these results, we build a model of the conditional probability distribution of DSCRs at each point in the life of infrastructure projects.
The objectives of this paper are to document the statistical characteristics of debt service cover ratios (DSCRs) in infrastructure project finance, and to develop and calibrate a model of DSCR dynamics. Advanced stochastic modelling of infrastructure project debt has the potential to considerably improve credit risk measures.