This paper drawn from the EDHECinfra /LTIIA Research Chair shows that common risk factors found in numerous asset classes explain the evolution of unlisted infrastructure secondary market prices. It also shows that after a long period of prices increases, “peak infra” may already be behind us.

We develop a structural credit risk model relying on cash flow data to derive credit risk metrics that 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.

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.