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Given a time series of prices for any relevant unlisted infrastructure asset (debt or equity), returns are computed as
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\hat{r}_{i,t,t+1} = \log \frac{\hat{P}_{i,t+1} + CF_{i,t+1}}{\hat{P}_{i,t}} 
where
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Modelimplied prices for the next period,
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\sigma(P_{i,t}) = \frac{\partial P_{i,t}}{\partial R_{f}} \sigma(R_{f}) + \sum_{k} \frac{\partial P_{i,t}}{\partial \lambda_{k}} \sigma(\lambda_{k}) + \sum_{k} \frac{\partial P_{i,t}}{\partial \beta_{i,k}} \sigma(\beta_{k}) 
where the first term,
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Thus, the estimated volatility of returns,
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 the volatility of the riskfreerate term structure,
;LaTeX Math Inline body \sigma(R_{f})
volatility of forecasted cash flows,
;LaTeX Math Inline body \sigma(CF_{i})  the uncertainty with which the prices of risk factors are estimated,
; andLaTeX Math Inline body \sigma(\lambda)  the uncertainty with which the factor loadings are estimated,
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These sources of uncertainty – cash flows, riskfree rates, expected risk premia, and factor loadings – capture the full range of uncertainty for the future price of an asset, and the volatility estimate summarises the combined effect of all these sources of uncertainty.
This volatility measure is thus constructed in a 'bottomup' fashion, by estimating and aggregating individual sources of uncertainty. Some assets may be risky because their cash flows are riskier (higher
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Depending on the investment objective, different sources of uncertainty may matter more or less for different investors, and they may justify different allocations to assets with different underlying sources of risk, even if they exhibit similar levels of aggregate volatility.
A more detailed discussion of the crosssectional volatility of the market index is available here.