The fallout of the 2009 financial crisis has triggered a slow but certain paradigm shift for the asset allocation decisions of institutional investors. The objective of diversifying away from market volatility along with the increasing role played by liability-driven investment is fuelling increasing interest in unlisted assets with long-dated maturities, predictable cash flows and attractive yields.

Infrastructure investment is amongst the areas that intuitively offer some of these appealing characteristics. Infrastructure debt is the main candidate for new allocations since it is useful both from a diversification and asset-liability management perspective for an insurer or pension fund. Moreover, as we discuss in a recent paper (Blanc-Brude, 2013), most infrastructure financing consists of debt financing. Hence, infrastructure debt is the most relevant area of investment from an institutional perspective.

However, the investment profile of these assets is not well documented and often ill-understood. Today, despite a few empirical studies, there does not exist any scientific benchmark of unlisted infrastructure debt credit risk.

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. Section 2 describes our intuition: 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.

Our intention is to develop risk measures for infrastructure debt that are both rooted in modern financial theory and implementable empirically because we know that the necessary data can be collected.

Hence, 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. In section 3, 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 the expected value E(DSCRt), the probability of default pt = Pr(DSCRt < 1.x|minj<t DSCRj ≥ 1.x) and the probability of emergence from default qt = Pr(DSCRt ≥ 1.x|DSCRt−1 < 1.x).

In section 4, 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 ànance 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 in section 5.

Finally, we 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.

For this purpose, in section 7, we propose a data collection template to facilitate the participation of investors and lenders to a data collection effort and improve the future benchmarking and transparency of infrastructure debt investments.