Strategic Asset Allocation with Unlisted Infrastructure

In this paper, we show how the traditional indexes used as proxies for unlisted infrastructure fail to represent the qualities of the asset class. Listed infrastructure indices are highly correlated with the wider equity universe – if the asset class behaved in this way, there would be little point in investors buying it as it would not add much in terms of diversification or improving the risk-return profile of the portfolio. Appraisal-based indices are correlated with nothing at all, making them singularly useless for the task in hand – their construction gives results that are so “smooth” that volatility is very low and correlations close to zero, which would signal unrealistically high risk-return rewards that are simply unfeasible in the real world. EDHECinfra’s indices of unlisted infrastructure, on the other hand, such as the infra300®, represent the characteristics of this asset class well, making them the best available proxy for investors to use.

We also show how investors can carry out a simple asset allocation exercise to calculate the optimal allocation they should be making to unlisted infrastructure based on their individual portfolio needs. Using different optimisation techniques and parameters, and considering different investor profiles, our research signals consistent allocations to infrastructure in the region of 10%, many times current levels. Our indices also offer a granularity that can help portfolio design in a way that broader and less well-defined proxies are unlikely to achieve for those seeking to optimise risk-adjusted returns.

The Pricing of Private Infrastructure Debt – a dynamic approach

This paper examines the drivers and evolution of credit spreads in private infrastructure debt. We ask two main questions:

Which factors explain private infrastructure credit spreads (and discount rates) and how do they evolve over time?
Are infrastructure project finance spreads and infrastructure corporate spreads driven by common factors?

We show that common risk factors partly explain both infrastructure and corporate debt spreads. However, the pricing of these factors differs, sometimes considerably, between the two types of private debt instruments.

We also find that private infrastructure debt has been `fairly’ priced even after the 2008 credit crisis. That is because spread levels are well-explained by the evolution of systematic risk factor premia and, taking these into account, current spreads are only about 29bps above their pre-2008 level. In other words, taking into account the level of risk (factor loadings) in the investible universe and the price of risk (risk factor premia) over the past 20 years, we only find a small increase in the average level of credit spreads, whereas absolute spread levels are twice as high today as they were before 2008.

Research for Institutional Money Management – Fall 2018

Investors hit a roadblock when investing in infrastructure. Until now none of the metrics needed by investors were documented in a robust manner, if at all, for privately held infrastructure equity or debt. This has left investors frustrated and wary. In a recent EDHECinfra/Global Infrastructure Hub Survey of major asset owners, more than half declared that they did not trust the valuations reported by infrastructure asset managers.
How, under such conditions, can the vast increases in long-term investment in infrastructure by institutional players take place? We need transparency and accurate performance measures.

This is the year of the Argentinian presidency of the G20 and it has been marked by a focus on infrastructure investment. With the support of the G20, the Singapore government, The Long-Term Infrastructure Investors Association, the Long-Term Investment Club and numerous private sector supporters, including Natixis, EDHECinfra has now built the largest database of infrastructure investment data in the world. With this we can now bring transparency and accurate performance measures to the infrastructure sector.

Using this data EDHECinfra has created performance benchmarks that are needed for asset allocation, prudential regulation and the design of infrastructure investment solutions. These first of a kind benchmarks provide investment metrics that are needed by investors; return, volatility, Sharpe ratio, duration, and maximum drawdown.
In 2019, this database will reach global coverage and a global index for private infrastructure debt and equity tracking 1000 firms can be published.

We started our journey to build benchmarks for infrastructure investors in Europe, the oldest and largest investible market for infrastructure in the world. We analysed the European market and selected the 14 major markets for infrastructure. We studied the size, age and evolution of the infrastructure industry in each of those countries, and painstakingly identified all investible infrastructure assets.

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.

2016 EDHEC-Risk Institute Research Insights

This special supplement of IP&E highlights the findings of new research on infrastructure investments that were presented at EDHEC-Risk Days 2016.

Drawing on research from the Meridiam/ Campbell-Lutyens research chair at EDHECinfra, we analyse the characteristics of cash flows in private infrastructure firms and find that infrastructure firms exhibit a truly unique business model compared to public and private rms. The equity payout behaviour of infrastructure firms is very different from that of other firms: infrastructure firms pay more often and in significantly higher proportions of their revenues than other firms once the lifecycle of the rm is taken into account. We conclude that infrastructure firms have significantly lower volatility of revenues and profits and pay a much higher proportion of their revenues much more frequently to their owners, independent of the business cycle.

On the subject of the cash flow dynamics of private infrastructure project debt, as part of the Natixis research chair at EDHECinfra, we produce new results using a new infrastructure cash ow database. We show that a powerful statistical model of credit ratio dynamics provides insights for the valuation of private credit instruments in infrastructure project nance. It also militates for standardising the data collection and computation of items such as the debt service cover ratio in infrastructure project nance, and for pooling this information in central repositories where it can be used to create the investment metrics that investors need (and regulators require) to be able to invest in large, illiquid assets such as private infrastructure project debt.

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.

Measuring infrastructure the credit risk of unlisted infrastructure debt

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.

infraMetrics® by EDHECinfra

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