Calibrating Credit Risk Dynamics in Private Infrastructure Debt

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.

You can work it out! Valuation and Recovery of Private Debt with a Renegotiable Default Threshold

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.

A Structural Credit Risk Model for Illiquid Debt

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.

Cash Flow Dynamics of Private Infrastructure Project Debt

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.

Cash flow dynamics of infrastructure project debt: Empirical evidence and dynamic modelling

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.

DISCLAIMER

  • The information contained on the EDHECinfra website (the “information”) has been prepared by EDHECinfra solely for informational purposes, is not a recommendation to participate in any particular investment strategy and should not be considered as an investment advice or an offer to sell or buy certain securities.

    All information provided by EDHECinfra is impersonal and not tailored to the needs of any person, entity or group of persons. The information shall not be used for any unlawful or unauthorised purposes. The information is provided on an “as is” basis.

    Although EDHECinfra shall obtain information from sources which EDHECinfra considers to be reliable, neither EDHECinfra nor its information providers involved in, or related to, compiling, computing or creating the information (collectively, the “EDHECinfra Parties”) guarantees the accuracy and/or the completeness of any of this information.

    None of the EDHECinfra Parties makes any representation or warranty, express or implied, as to the results to be obtained by any person or entity from any use of this information, and the user of this information assumes the entire risk of any use made of this information. None of the EDHECinfra Parties makes any express or implied warranties, and the EDHECinfra Parties hereby expressly disclaim all implied warranties (including, without limitation, any implied warranties of accuracy, completeness, timeliness, sequence, currentness, merchantability, quality or fitness for a particular purpose) with respect to any of this information.

    Without limiting any of the foregoing, in no event shall any of the EDHECinfra Parties have any liability for any direct, indirect, special, punitive, consequential or any other damages (including lost profits), even if notified of the possibility of such damages.

    All EDHECinfra Indices and data are the exclusive property of EDHECinfra. Information containing any historical information, data or analysis should not be taken as an indication or guarantee of any future performance, analysis, forecast or prediction. Past performance does not guarantee future results. In many cases, hypothetical, back-tested results were achieved by means of the retroactive application of a simulation model and, as such, the corresponding results have inherent limitations.

    The Index returns shown do not represent the results of actual trading of investable assets/securities. EDHECinfra maintains the Index and calculates the Index levels and performance shown or discussed, but does not manage actual assets. Index returns do not reflect payment of any sales charges or fees an investor may pay to purchase the securities underlying the Index or investment funds that are intended to track the performance of the Index. The imposition of these fees and charges would cause actual and back-tested performance of the securities/fund to be lower than the Index performance shown. Back-tested performance may not reflect the impact that any material market or economic factors might have had on the advisor’s management of actual client assets.

    The information may be used to create works such as charts and reports. Limited extracts of information and/or data derived from the information may be distributed or redistributed provided this is done infrequently in a non-systematic manner. The information may be used within the framework of investment activities provided that it is not done in connection with the marketing or promotion of any financial instrument or investment product that makes any explicit reference to the trademarks licensed to EDHECinfra (EDHECinfra, Scientific Infra and any other trademarks licensed to EDHEC Group) and that is based on, or seeks to match, the performance of the whole, or any part, of a EDHECinfra index. Such use requires that the Subscriber first enters into a separate license agreement with EDHECinfra. The Information may not be used to verify or correct other data or information from other sources.

    The terms contained in this Disclaimer are in addition to the Terms of Service for users without a subscription applicable to the EDHECinfra website, which are incorporated herein by reference.

    This site uses cookies to deliver the services you request, improve user experience and measure audience. By continuing to browse our website, you are consenting to our use of cookies. Find out more about this in our Privacy policy.

  • X
    X