Asset prices are computed each quarter by EDHEC*infra* using a discounted cash flow approach and discount rates calibrated from secondary market infrastructure equity transactions over time.

The EDHEC*infra* Broadmarket Unlisted Equity Index is a __calculated index__. The prices used in the index are computed directly from available cash flow and market data, using a unified asset pricing methodology.

The market value of the constituents included in the index are computed using a discounted cash flow (or income) methodology, using company-level information to forecast dividend payouts and shareholder loan repayments. A factor model of expected returns is calibrated to reflect the latest market price of risk and to determine the appropriate discount rate.

Thus, at any time t we have:

P_i=\frac{\sum_{t=1}^T CF_t}{\big(1+(R_f+E(\tilde{R_i}))\big)^t} |

P_i is the price of asset i, paying CF_t until time T. R_f+E(\tilde{R_i}) is the approximate expected internal rate of return (IRR) at time t.

### Cash Flow Data

For each company included in the index, the minimum required data described in the Data Collection Standard is collected and aggregated are categorised according to the TICCS® classification.

This data is then used to produce several forecasts:

- A Revenue Forecast is determined by human analysts and cross-validated;
- A Total Debt Service Forecast is determined by human analysts and cross-validated;
- A Cash Flow Available for Debt Service (CFADS) forecast is made using a statistical model that takes the revenue and debts service forecasts a inputs
- A Free Cash Flow to Equity Retention Rate (RR) forecast is made using a statistical model that take into account future debt service and the lifecycle of the company

Future dividends are derived as follows

Dividend_t = FCFE_t \times (1 - {RR}_t) = max(0, CFADS_t - DS_t) \times (1 - {RR}_t) |

Each company's dividend forecast at time t is then used to compute a price using a discounted cash flow model taking two other inputs: a term-structure of interest rates and a risk premia.

### Market Rates Term Structure Data

Interest rate data for each available horizon are interpolated using a standard methodology (see Asset Pricing Methodology) to derive a term structure of risk-free rates on each relevant future valuation date.

Interest rate data: Datastream®

### Risk Premia Data

The mark-to-market risk premia applicable to each company to be priced, on each valuation date, is estimated by **observing secondary market** **internal rate of return** (IRR) and statistically estimating the effect of certain systematic risk factors e.g. size, leverage, profits, etc. as well as TICCS® sector and business model control variables.

E(\tilde{R_{i}})-R_f=\lambda_{1} \beta_{i,1}+\dots+\lambda_{K}\beta_{i,K} + \omega_i |

where \lambda_k is the price or premia of each K risk factor, \beta_{i,k} is the factor loading or exposure of company i to factor k, and \omega_i is the measurement noise introduced when estimating E(\tilde{R_i}).

Once these risk premia have been estimated over time, each one is used to derive a mark-to-market risk premia for each individual company i.e. given its size, leverage, profits, TICCS® classification, etc. at the time of valuation.

E(\hat{R_{j}})-R_f=\sum_k \hat{\lambda_{k}} \beta_{j,k} |

The prices of all infrastructure equity stakes obtained using this approach are then used to compute the asset-level performance and risk metrics.

Index data and analytics are then computed using these results.

More details are available below and in the supporting documentation.