Credit Risk Measurement: Alternatives for PD-LGD-EAD on the Horizon?
Probability of default (PD), loss-given default (LGD) and exposure at default (EAD) have been the go-to methodologies for credit risk measurement for the past four decades. However, they are imperfect, and there seems to be room for the emergence of a new, more powerful and more accurate framework for assessing default risk.
What might this alternative credit risk system look like? One strong possibility is a cash-flow-based model that uses machine-learning algorithms. That type of framework would have more computational power, and potentially could be superior at predicting defaults than traditional tools. Before we further examine this alternative, it’s important for us to understand the current uses of the PD-LGD-EAD framework, as well as how we have arrived at this stage.
The PD-LGD-EAD risk parameters are crucial factors in the calculation of both expected loss (per exposure) and, after summation, portfolio expected loss. They also provide the raw material for calculating risk-weighted assets (RWA). Though the RWA formula is more involved than a simple multiplication, it is still easily implementable in computer code or even via a spreadsheet application. Just like expected loss, moreover, the RWA per exposure is additive from the individual exposure to the portfolio level.
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