Credit-Loss Forecasting: A Practical Guide to CECL

The COVID-19 crisis found most financial models wanting. In response, modelers panicked: what models, and how many of them, were now needed to cope with the “new normal”? Adding more regression models with different drivers won’t help capture tail risk in advance. Applying models calibrated to historical data to stress scenarios is simply wrong. In my latest column for GARP, I outline an alternative way of generating multiple, dynamic scenarios, which are usable from CECL to strategic planning. For the full article, check out the latest issue.