1.4.3 Debt spreads

Key points

Infrastructure debt spreads are reported directly in the primary and secondary markets.
A risk factor model of credit spreads is estimated for all available price information at each point in time.
A mark-to-market spread for each debt instrument to be priced at each point in time is estimated.

Unlike unlisted equity investments for which only prices are observable, infrastructure debt spreads are reported directly in the primary and secondary markets. As is the case for the valuation of equity investments, a risk factor model of credit spreads is estimated for all available price information at each point in time.

Next, the estimated risk premia that explain observed spreads are used to compute a mark-to-market spread for each debt instrument to be priced at that time, given their individual factor loading.

Spreads are obtained from primary and secondary market sources and regressed against individual asset factor loadings (betas) to estimate individual factor prices () in the cross-section, at that point in time. Thus, we have

where is the measurement noise introduced when estimating . In other words, following the APT equation, we can write spreads at time as a function of factor loading and factor prices plus some measurement error.

Using a range of statistical techniques (e.g. Bayesian regressions), the value of can be estimated for observable transactions at time (the cross-section of transactions).

Estimated then gives us new estimates of the spread for all assets in the relevant period in accordance with their individual betas, including those assets for which no transaction prices were available at the time, so that .

Hence, our approach consists of predicting the prices of risk factors that apply to assets with certain characteristics even though they have not been traded in the relevant period.

Choice of risk factors for private infrastructure debt

The following systematic five key risk factors are used by Scientific Infra & Private Assets to estimate the model of the expected returns using observable market prices as inputs, as well as several control (dummy) variables that account for sector and business model-specific effects.

_{Factor Name}	_{Factor Definition}	_{Factor Interpretation}
_Size	_{Outstanding Face Value}	_{Larger instruments tend to have lower spreads confirming a known stylised fact from the empirical literature on credit spreads (for non-price characteristics of loans, see Strahan, 1999). By this hypothesis, lenders extend larger debt instruments to lower credit risks.}
_Maturity	_Maturity	_{Instruments with longer maturity are more sensitive to interest rate movement and attract}_{positive risk premia}_{and higher spread for infrastructure corporates, but also}_{lower risk premia}_{for infrastructure project finance loans. This is also in line with long-standing findings in the empirical literature on spreads, including project finance loans.}
_{Credit Risk}	_{Probability of Default}	_{Higher credit risk, as estimated by our borrower-specific credit risk model, commands}_{positive risk premia}_{and higher spread.}
_{Short Term Rates}	_{3-Month interest rate}	_{Likewise, controlling for other effects, movements in short-term interest rates impact}_{risk premia}_.
_{TICCS® Business Risk}	_{Merchant, Regulated or Contracted control variables}	_{Controlling for business risk families as defined in TICCS® shows that merchant companies systematically attract higher risk premia, i.e. expected returns are higher in riskier segments of the infrastructure market.}
_{TICCS® Sector}	_{Industrial activity superclass or class control variables}	_{Likewise, a few sectors are found to have systematically higher or lower expected returns even after controlling for the effect of the factors described above, e.g. renewable energy projects have systematically lower returns (or higher prices) even for similar size, profits, leverage etc.}

_{Strahan, Philip E. "Borrower risk and the price and nonprice terms of bank loans."}_{FRB of New York staff report}_{90 (1999).}