Measuring infrastructure the credit risk of unlisted infrastructure debt — theoretical framework and data reporting requirements

by Frédéric Blanc-Brude, Omneia R H Ismail
Abstract:
In this paper, we develop a framework to measure the credit risk of unlisted infrastructure debt, including the first formulation of “distance to default” in infrastructure project finance. We propose to use the debt service cover ratio (DSCR or the ratio of the firm’s free cash flow to its debt service in a given period), which is routinely collected by project finance lenders, to measure and benchmark credit risk in infrastructure project finance. We argue that knowledge of the first two moments of distribution of the DSCR in project finance are sufficient to measure and predict the credit risk of individual loans. We show that the distribution of the DSCR captures asset value and volatility and allows measuring distance to default in project finance. The distribution of the DSCR also provides an unambiguous default point and can thus be used to build a mapping of expected default frequencies (EDFs) in project finance. Once characterised, the distribution of the DSCR allows the computation of an expected value, a condi- tional probability of default at time t and a conditional probability of emergence from default. We show that these variables are sufficient to compute loss given default (LGD) and the expression of a loss density function of project finance loans at each point in the project lifecycle. Thus, the knowledge of the distribution of the DSCR in project finance allows the calculation of a value-at-risk (VaR) measure of infrastructure project debt, which can be used, for example, to calibrate a risk module, such as those used in risk-based prudential frameworks. We highlight the relevance of our conclusions with an illustrative simulation. We also conclude that a large sample of observed or simulated project debt cash flows and their respective DSCR in each period, could be used to derive either a functional form for the distribution of the DSCR or an empirical mapping of distance to default and probabilities of default in project finance at each point in the project lifecycle. Thus, we propose a template to support data collection initiatives and improve the future benchmarking and transparency of infrastructure investments.
Reference:
Measuring infrastructure the credit risk of unlisted infrastructure debt — theoretical framework and data reporting requirements (Frédéric Blanc-Brude, Omneia R H Ismail), In EDHEC Business School Working Paper, 2013.