Sometimes you perceive something as a major risk, but the reality exposed by the data just doesn’t live up to your expectations. This is, in fact, what many modelers (myself included) experienced during the pandemic: expectations were constantly challenged by data.
Over the next 12 months, as COVID slowly wanes as an economic disruptor, a lot of industry credit risk models will be redeveloped. Historically, these methods have provided superior predictions to those based on gut feelings – but most of them were sidelined during the pandemic, because they were unable to explain the odd behavior that was unfolding. At some point, though, model owners will need to grapple with the unusual data and rehabilitate their quantitative models.
Last month, the European Central Bank published the results of its monumental TRIM project – a detailed five-year exercise to assess the internal models used by large banks to determine risk weights and regulatory capital charges.
As the world awakens, one of the vexed questions we face as model risk managers is when to redevelop. The simple fact is that the established procedure of designing and building a model, and then having it validated and implemented into various IT processes, is very cumbersome, time consuming and expensive.
The COVID-19 saga has caused real difficulties for risk modelers. Loss projections made using pre-pandemic models soared in mid-2020, as global economic data spiraled downward. Portfolio performance, however, has held up very well under extremely difficult circumstances.
When it comes to both the present and the future, which financial institutions are in a better position: those that rely on traditional, structural credit risk models or those that lean toward machine-learning models? Accuracy, intuitiveness, adaptability and behavioral shifts are among the factors that should be evaluated when answering this question – but we must first consider how different types of models have performed in 2020.
2020 has been a challenging year for risk modelers, rife with uncertainty. What have we learned about the effectiveness of existing stress tests and the scenarios that drive them?
Looking in the rearview mirror, it’s usually easy to see whether previously accepted risks were adversely realized. The moment you pay back the $20 I loaned you last month, for example, I know that the credit risk associated with our transaction has cured.