Making a Better Match: Institutional Investors and Infrastructure
Matching up infrastructure projects and institutional investors is difficult. Better knowledge of how infrastructure assets will behave and new ways to monitor performance can help.
February 2015 | by David Delaney
Why is it so difficult to match long-term money with long-term investment projects such as new infrastructure? Policy makers have certainly made a priority of the search for new ways to finance long-term growth. At the same time, institutional investors have recognized the need for alternative long-term instruments to help meet long-term commitments such as pension payouts or insurance policies. Yet matching investment demand (for new infrastructure) and supply (from institutional investors) remains elusive.
We propose a step-by-step road map to help resolve the question of how relevant to investors infrastructure investment can be. Our approach requires a multi-stakeholder effort to reveal the characteristics of infrastructure assets at the underlying and portfolio levels and reduce existing information asymmetries between investors and managers. This road map has five elements:
Valuation and risk-measurement methodology. With a clear and well-accepted definition of underlying instruments, it is possible to develop adequate valuation and risk-measurement methodologies that take into account infrequent trading. By “adequate” we mean that such methodologies should rely on the rigorous use of asset-pricing theory and statistical techniques to derive the necessary input data, while also aiming for parsimony and realism when it comes to data collection. The proposed methodologies should lead to the definition of the minimum data requirement, which is necessary to derive robust return and risk estimates.
Data-collection requirements. While ensuring theoretical robustness is paramount to the reliability of performance measurement, a trade-off exists with the requirement to collect real-world data from market participants. In particular, proposed methodologies should aim to minimize the number of inputs in order to limit the number of parameter-estimation errors. Adequate models should also focus on using known data points that are already collected and monitored or could be collected easily. In all cases, data requirements should be derived from the theoretical framework, not the other way around. In turn, whether the necessary data already exist or not, this process will also inform the standardization of investment-data collection and reporting.
Reporting standards. Standardizing infrastructure-investment data collection should enable the emergence of an industry-wide reporting standard that investors and regulators alike can recognize. Such a reporting standard would increase transparency between investors and managers, who would now be mandated to invest in a well-defined type of instrument and commit to report enough relevant data for investors to benefit from their specialized monitoring.
Investment benchmarks. The investment profile of the underlying assets spans expected returns, risk, and market correlations. Once these have been documented as well as the existing data allow, it is possible to design investment benchmarks to reflect the performance of a given strategy (for example, maximum Sharpe Ratio) for a given investment horizon.
Regulation. The robust performance benchmarking of unlisted infrastructure equity portfolios also has direct regulatory implications for risk-based prudential frameworks like Solvency II, the directive codifying EU insurance regulation. For example, the benchmarking should permit calibrating a dedicated unlisted infrastructure submodule in the context of the Solvency II formula, or usefully informing investors’ internal risk models.
At Burk, we have begun following this road map. In our publications, both recent and upcoming, we propose a number of solutions to make infrastructure investment more relevant to institutional investors. As a first step, we suggest that well-defined underlying instruments can be found in project-finance debt and equity, which embody many of the aspects of the investment narrative and can be modeled and calibrated.