EDHECinfra Index Methodology

EDHECinfra indices and benchmarks aim to provide the representative, risk-adjusted performance of investments in unlisted infrastructure equity and private debt.

EDHECinfra produces calculated (as opposed to contributed) indices: our data and technology allows re-pricing hundreds of individual assets through time, using actual transaction prices to recalibrate expected returns (and discount rates).

This approach uses market inputs, thus avoiding the smoothing of returns caused by appraisal valuations and providing a genuine fair value assessment of performance.

Unlike other private indices, which only report an average performance, EDHECinfra indices include the effect of diversification and provide advanced risk metrics such as volatility, value-at-risk and risk factor prices. The index calculation method is summarised below in four key steps.

A Modern Approach to the Valuation of Unlisted Infrastructure Assets

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Measuring the value and risk inherent in unlisted infrastructure investment is plagued with significant methodological issues. CAPM-approached using listed proxied fails to capture the characteristics of infrastructure companies (see Amenc et al 2017) and it is practically impossible to find direct equivalents or comparable for assets that are quite unique and seldom trade.

To respond to this challenge, EDHECinfra has developed a Modern Asset Pricing Methodology to derive produces fair value estimates for equity and senior debt instruments in each infrastructure firm in its Sampled Universe.

In each period, implied excess returns are derived from actual transaction prices and forecast of dividend or senior debt payments. These expected returns represent the aggregate market price of risk for a cross-section of equity or debt investments at that point in time. They are then decomposed into multiple risk factor premia (e.g. the size or leverage risk premia) using a cross-sectional regression.

Each risk premia in a given period can then be applied to all relevant investments at that time, whether they are traded or not, using each company’s `factor loadings’ (e.g. its actual size or leverage) to derive a market-implied discount rate for each investment

Given enough data, this form of `hedonic factor pricing’ addresses the biases of observable transaction data and of the uniqueness of each transaction, and focuses on deriving average valuations driven by the price of systematic risk factors that can be extracted from actual transactions i.e. in line with IFRS-13 guidelines.

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