When narrative scenarios first became a standard tool for risk management, around the time of SCAP in 2009, I was frankly skeptical that the technique would last. Having emerged from academia, I was used to more rigorous methods and had spent years coding up very detailed and complex Monte Carlo experiments. I always thought that simulation methods, aided by advances in computing power, would eventually replace narrative scenarios for stress testing.
Category: Risk Modelling
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.
In credit risk management, it is common to distinguish between point-in-time (PIT) and through-the-cycle (TTC) estimates of default probability or expected loss. But amid a unique pandemic, TTC loss forecasts may have been too bullish, and there is now talk about whether this credit risk estimation tool needs to be fine-tuned to reflect financial institutions’ new reality.
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.
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?
What is a forward-looking model?
It is now clear that a deep COVID-19 recession will hit the global economy this year. This event, completely distinct from anything that has happened in living memory, will bring a new set of challenges, and a host of opportunities, for risk modelers around the world.