In this paper, we present the first results of a multiyear project to create and compute fully fledged private infrastructure debt investment benchmarks. The first version of these indices span 14 European countries over 16 years, going back to 2000. They are built from a representative sample by size and vintage of the private European infrastructure debt market, including hundreds of borrowers and debt instruments over that period.
In particular, we focus on what distinguishes infrastructure debt from corporate debt. When developing this research, we used two competing views of what defines infrastructure investment:
The first one equates infrastructure investment with “project finance”¹ and echoes the June 2016 advice of the European insurance regulator (EIOPA, 2016) to the European Commission to define ”qualifying infrastructure” for the purposes of the Solvency-II directive;
The second view, also expressed during recent prudential regulatory consultations, defines infrastructure investment more broadly and proposes to include “infrastructure corporates” to the definition of qualifying infrastructure assets, effectively arguing that a number of firms – because they operate in industrial sectors corresponding to real-world infrastructure – constitute in themselves a unique asset class, with its own risk/reward profile.
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.
This paper is part of an ongoing research project aiming to create long-term investment benchmarks for investors in infrastructure. It is the first valuation and risk measurement model created specifically for unlisted infrastructure debt instruments. It provides a framework to value and assess the return and risk characteristics of individual project finance loans.
Part of the EDHEC/Natixis Research Chair 2012-2015
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, which is routinely collected by project finance lenders, to measure and benchmark credit risk in infrastructure project finance. We have shown that full knowledge of the distribution of the DSCR in infrastructure project finance is sufficient to characterise the credit risk of infrastructure project debt. In this paper we also propose a template to support data collection initiatives and improve the future benchmarking and transparency of infrastructure investments.
In July 2013 the Central Bank of Ireland issued a discussion paper on loan origination by investment funds, in which it suggests that developing alternative sources of financing to bank loans may be beneficial to the real economy but requires the careful consideration of the potential development of “shadow banking” risks.
In this response to the discussion paper, we argue that the development of alternative sources of financing is most relevant with regards to long-term private debt, in particular the financing of SMEs and infrastructure projects. The demand for such financing has been identified as instrumental to long-term growth in Europe, which justifies regulatory changes.
This paper is the first of series discussing the opportunity for long-term institutional investors such as pension funds, insurance companies or sovereign wealth funds, to invest in large portfolios of infrastructure debt, both to manage their liabilities and to minimise their exposure to capital market volatility. Our analysis focuses on project finance debt since it represents the bulk of existing and, in all likelihood, future infrastructure debt.
In what follows, we review existing academic research on infrastructure project finance and propose a theoretical and empirical analysis of the role of credit risk in infrastructure debt from a portfolio standpoint, on a held-to-maturity basis.