We do not consider tax implications. Even without profit, tax credits can significantly contribute to mitigating the impact on capital.

- **NPL composition and provisioning.** A key assumption is that the NPL portfolio is equally divided among substandard, doubtful, and loss loans. We further assume that the corresponding provisioning rates are 20, 50, and 100 percent. As a result, we hypothesize an overall provision rate of 55 percent for all banks. This assumption could be relaxed using the information at the country level on provisions to nonperforming loans or using granular bank-level data on provisioning or on the ratio of the difference between gross loans and net loans over gross loans.

- **No collateral.** We assume that loans are not collateralized or that collateral cannot be liquidated sufficiently quickly.

- **Minimum capital requirements.** Data on country minimum regulatory capital requirements refer to year-end 2016 and are taken from the World Bank’s Bank Regulation and Supervision Survey. The minimum capital requirement is applied equally to all banks and does not take into account specific capital buffers (e.g., capital conservation buffer and capital buffers for systemically important institutions). Effective capital requirements may be also higher due to, for example, leverage ratio requirements (i.e., requirements on bank capital to unweighted assets ratio) or addons following supervisory stress test exercises.

Further, the empirical approach presents the following limitations:

- **Interdependence structure.** The CDBP does not capture non-linear distress dependencies among the banks in the banking system (Goodhart & Segoviano, 2009). For example, it does not incorporate knock-on effects and balance sheet interlinkages that may lead to contagion.

- **Type of shock.** The system-wide bank instability measure accounts for increases in credit risk. Other important sources of risk in EMDEs could be the sovereign-bank nexus (Feyen & Zuccardi, 2019), foreign exchange rate risk (Demirguck-Kunt & Detragiache, 1998), a sudden stop of capital inflows and sharp depreciation (Reinhart & Rogoff, 2013), and volatility in commodity prices (Eberhardt & Presbitero, 2021; Kinda et al., 2018).

- **Likelihood of the increase in NPLs.** The approach says little about the likelihood of occurrence of the potential increase in non-performing loans due to the credit shock (or recognition of the true performance of the actual portfolio). The reverse stress test mechanically computes the NPL breaking point without attaching an estimate of the likelihood for a bank of such occurrence. It is also silent on the time horizon over which the increase in NPLs materializes.

- **Quality of NPL data.** NPL data are backward-looking and come with a lag. NPL data may be underreported, particularly in countries with weaker supervisory practices.

- **Sample coverage.** The bank sample may be incomplete, especially in some countries and for specific bank types such as unlisted banks (including mutual banks or state-owned banks), smaller banks, rural credit institutions, and other credit institutions.

# 3. Application to the COVID-19 pandemic

The macro-financial shock caused by the COVID-19 pandemic precipitated a global economic recession in 2020 and put severe pressure on financial markets and institutions around the world. After the initial shock, many economies have (partially) recovered but the COVID-19 health crisis may produce long-lasting negative effects on banking systems, especially in EMDEs. Uncertainty in the development of the health crisis and its negative spillovers are likely to weigh heavily on banking sectors and are expected to lead to 7