RDP 2022-03: Macrofinancial Stress Testing on Australian Banks 6. How the Model Was Used during COVID-19

The onset of the COVID-19 pandemic in early 2020 triggered widespread concern about how resilient banks would be to the ensuing economic downturn. There was a significant risk that large numbers of households and businesses would be unable to continue servicing their loans, given the impending drop in income, creating uncertainty about how much bank capital could be subsequently eroded. This event provided a clear instance of when the stress testing model was useful.

One way in which the model was used during this period was to run a stress test using downside economic scenarios published by the RBA's Economic Analysis Department in the August 2020 SMP. This scenario involved a more than 10 per cent fall in GDP and the unemployment rate rising to above 10 per cent. In addition, the stress testing model assumed a 20 per cent fall in property prices. Importantly, the scenario assumed that bank funding costs remained steady because of the support available from the RBA's Term Funding Facility, which ensured the effect of the scenario on pre-provision profit was minimal. Nonetheless, with rising loan loss provisions, banks' capital ratios were forecast to decline by almost 200 basis points (Figure 9).^[37]

Variations around this exercise also revealed interesting insights into the scenarios that would create more stress for banks. One insight is that credit losses accelerate in a nonlinear fashion as the economic shock deepens. That is, bank capital is estimated to fall by more than twice as much in a scenario in which all variables deteriorated two times more than in the baseline scenario. However, the extent of nonlinearity is not as pronounced as might be expected based on LGD being a highly nonlinear function, perhaps because most loans in Australia are so well collateralised and so it takes very extreme shocks to move a large share of loans into a position of negative equity. A second insight was that losses are inflated when the economy deteriorates along multiple dimensions (such as property prices falling in addition to economic activity weakening). This finding is consistent with the ‘double trigger’ hypothesis discussed above – that losses only arise when incomes fall and collateral is eroded, not when one happens independently of the other. Thirdly, the modelling showed that the speed at which the economy deteriorates also matters for bank resilience. Specifically, the model predicts that a very large deterioration in the economy that arises in just a few quarters, before the economy begins to recover, can be more damaging than when the same-sized deterioration occurs over a long period of time. This outcome is a direct result of the resilience that comes from banks' underlying profitability: when the economy weakens over a prolonged period, it gives banks more time to accumulate pre-provision profits that can offset the cumulative loss from defaults. In contrast, a sharp deterioration in the economy that causes losses to be concentrated in a few quarters can quite quickly result in banks making large quarterly losses that erode their capital.

Another important way that the stress testing model was used during the COVID-19 pandemic was in reverse. Specifically, the model is able to generate a scenario that produces a pre-specified outcome for bank capital ratios, reversing the conventional approach of pre-specifying a scenario and calculating the resultant effect on capital. This capability had considerable advantage over conventional stress testing during a period of heightened economic uncertainty. In particular, it enables the modeller to be somewhat agnostic about exactly how the economy will evolve and instead focus on what types of scenarios would cause bank capital levels to fall to excessively low levels. Technically, this is done by running multiple iterations of the model, with the scenario deteriorating each time (in a manner that ensures the profiles for GDP, the unemployment rate and property prices are internally consistent), until the model finds a scenario that hits the pre-specified capital ratio (for either one bank or the average of all banks). This procedure is computationally simpler than ‘inverting’ the stress testing model to determine the full distribution of scenarios for a given capital ratio outcome, as is done by some other central banks. As a result, it can be done very quickly.

The reverse stress test approach was undertaken within the first month of the pandemic affecting Australia, and was published in the April 2020 Financial Stability Review. Given that the key question at that time was how long the pandemic would disrupt economic activity, the reverse test involved an assessment of how many quarters of severe declines in GDP (around 5 per cent per quarter) would be required for banks to breach their prudential minimum requirements. The results of this exercise showed that Australian banks are very resilient: the model calculated that the recession would need to persist for more than a year at that severe pace of decline, lifting the unemployment rate to between 15–20 per cent, before a bank breached its prudential capital requirement. In the October 2020 Financial Stability Review, a reverse stress test was used to consider how deep – rather than prolonged – the recession might need to be for at least one bank's CET1 capital ratio to fall below 6 per cent. The slight difference between this question and that posed in April 2020 was a direct result of the observation note above, that sharp deteriorations in economic activity can be more damaging that equal-sized prolonged downturns. However, on this occasion the results were similar: the unemployment rate would need to rise to about 20 per cent and property prices fall by 50 per cent before a major bank's CET1 ratio would fall below 6 per cent.

Of course, such extreme scenarios are associated with considerable parameter uncertainty and so need to be interpreted as indicative, rather than precise. Moreover, improvements to the model's ability to forecast business credit losses since then and changes to the model formulation of risk weights have changed the size of the shock that would be required to cause some banks to breach their prudential minimums. Nonetheless, this exercise provided assurance that banks were financially strong enough to support the economy through the pandemic, and demonstrated the value of developing flexible top-down stress testing models during tranquil economic times.

Footnote

Note that this is the effect estimated when the modelling was initially done. Since then, changes to the model mean that the result of modelling the same scenario today would be somewhat different. [37]