Estimating credit risk in unlisted infrastructure debt can be challenging because actual defaults are rarely observed. Due to the significant control rights of lenders in infrastructure project finance in particular (aka step-in rights) action is often taken by lenders before a default of payment takes place. 

Looking at raw data, the debt service cover ratio (DSCR) of infrastructure companies seldom goes below 1, which would be the point of 'hard default'. Instead, it typically bounces back whenever it approaches 1, suggesting that low DSCRs do trigger 'credit events' (soft defaults) and restructurings by creditors.

To understand the extent and dynamics of credit risk, therefore, it is necessary to go beyond observing events of defaults under a 'reduced form' approach as is often done (see for example [1]). Instead, we adopt a structural approach i.e. modelling the default mechanism directly. 

This approach was pioneered by Blanc-Brude et al in a series of papers [2][3][4]Our credit risk methodology extends this approach: it calibrates the implied volatility of the cash flow available for debt service (CFADS) to measure the credit risk of the evaluated infrastructure company. A number of credit analytics including default risk (PD), loss given default (LGD) and more can be computed on the basis of the implied volatility of CFADS.

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