After a surprising level of resilience to a series of shocks in the first quarter, EM assets have finally capitulated over the past month, with nominal EM yields rising above 7% on the back of tightening global financial conditions, a worsening growth/inflation mix and rising debt risks. The IMF now estimates that 60% of low-income countries are at a high risk of debt distress.
To quantify which emerging and frontier markets may be most vulnerable to a continued rise in global risk aversion, we updated our Debt Sustainability Index earlier this week and repeat the exercise below for our External Liquidity Index (last updated in March) to quantify the risk of an external debt/balance of payments crisis across 43 emerging and frontier markets.
The first table shows the data in raw form, and the second scores each variable in terms of standard deviations better (worse) than the sample median and takes the simple average across variables to arrive at a composite external liquidity score (with lower/more negative numbers corresponding to greater vulnerability):
Note: To request access to all the data from our External Liquidity Index, please click here.
Results
The resulting output is a quick and dirty way to quantify the relative risk of an external debt or balance of payments crisis across in-sample countries, or vulnerability to a sudden stop in capital flows. After removing Sri Lanka (which has already defaulted) and Russia, Ukraine and Belarus (due to the impact of the ongoing war and associated sanctions), we find that the most vulnerable quintile of countries is Ethiopia, Tunisia, Egypt, Mongolia, Pakistan, Bolivia and Kenya. The least vulnerable quintile is Azerbaijan, Iraq, Peru, Uruguay, Bahrain, Brazil and Oman.
The chart below shows the biggest movers relative to the update following the fall WEO in November.
A simple scatter plot shows that there is a negative relationship between the overall external liquidity score and the country risk premium (measured by the EMBI), as we should expect. A linear line of best fit is statistically significant at the 99% confidence level with an R2 = 43.7% (though this drops to 98% and 19.3%, respectively, if we exclude countries with a spread over 1,500bps as outliers). A log-linear line of best fit is also statistically significant at the 99% confidence level with a slightly lower R2 = 39.5%, but has a slightly better fit when outliers are excluded (significant at 99% confidence level with R2 = 19.7%).
We also back-test our model by regressing the external sustainability score from November by the change in spread for each country-specific EMBI index since (excluding Ukraine). We find that the external liquidity score in November explained 39.6% of the change in spreads over the subsequent six-month period at a 99% confidence level, with each standard deviation difference in score translating to a 412bps difference in performance.
When countries whose EMBI spread has changed by more than 500bps are excluded as outliers, the model explains 14% of the variation in spreads and is significant at a 95% confidence level, with each standard deviation difference in score translating to a smaller 111bps difference in performance, suggesting that the index is most useful at flagging cases of extreme risk.
If EM assets continue to sell off, which we see as a distinct possibility (see here and here for more detailed thoughts), our debt sustainability and external liquidity indexes can, when paired with more detailed country-level fundamental analysis, serve as a warning for which countries may be most vulnerable to a debt or BOP crisis and/or which countries will most likely underperform the index in the months ahead, and should consequently be avoided.
Methodological issues
While we appreciate our model is highly stylised, the simplified and transparent approach is part of its appeal and we find it mostly offers intuitive results (and, where it does not, this can be a signal for further investigation). Of course, we urge our readers to take this data and its conclusions with a pinch of salt, and caution that it should be used in conjunction with traditional country risk analysis.
We also recognise some drawbacks of this approach. It fails to account for potential nonlinearities and threshold effects within variables, while equal weighting may ignore potential differences in importance. In addition, more timely and thorough data can be found for many countries using official sources, providing a more complete snapshot (but making cross-country comparison more difficult).
Data availability and the vintage of the available data is another challenge, though most of our indicators were deliberately chosen for their forward-looking or high-frequency nature. The longer the lag, and less contemporaneous the data is, the less useful it is as an early warning indicator.
We also omit other indicators which might be a cause of or signal distress, including political and institutional factors. However, we have chosen a more general approach to allow for cross-country comparison, and think our scorecard serves as a useful warning light for external stress.
We welcome feedback from our readers on methodology and coverage and remain available to answer any questions.
Appendix: Data explanations and sources
Gross FX reserves (months of import): Common international reserve benchmark, calculated in this instance by dividing gross foreign exchange reserve holdings by the trailing 12-month average of monthly imports (rather than forward-looking 12-month estimate of goods and services imports, which is more complete but for which data was lacking). Net reserves or total reserves (gross FX + gold) are possible alternative reserve indicators, subject to data availability.
Source: IMF International Financial Statistics (via Bloomberg) for reserves and IMF Direction of Trade Statistics (via Bloomberg) for imports. Frequency: Monthly (ranges from December 2021 to April 2022 for reserves and January 2022 for imports)
Total reserves (% of ARA metric): Total reserves (FX reserves + gold) as % of the IMF’s Assessing Reserve Adequacy metric (see here for details). IMF benchmark is 100-150% of ARA metric.
Source: IMF Assessing Reserve Adequacy DataMapper. Frequency: Annual (2022) for ARA metric and monthly (ranges from December 2021 to April 2022) for total reserves
External principal payments / reserves: Public and public guaranteed principal payments due to external creditors in 2022 relative to gross FX reserves (see above).
Source: World Bank International Debt Statistics. Frequency: Annual (2022)
Bond principal payments / reserves: Public and public guaranteed principal payments due to commercial external bondholders (incl. Eurobonds) in 2022 relative to gross FX reserves (see above).
Source: World Bank International Debt Statistics. Frequency: Annual (2022)
External debt service / exports: Total public and public guaranteed debt service (principal + interest) relative to 12-month trailing exports of goods. Exports of goods, services, and remittances may be a more useful denominator, subject to availability.
Source: World Bank International Debt Statistics for external debt service and IMF Direction of Trade Statistics (via Bloomberg) for goods exports. Frequency: Annual (2022) for external debt service and monthly (January 2022) for exports
External debt service / revenue: Total public and public guaranteed debt service (principal + interest) relative to projected government revenue.
Source: World Bank International Debt Statistics for external debt service and IMF April 2022 WEO for government revenue. Frequency: Annual (2022)
EMBI spread (basis points): Proxy for refinancing risk. Spreads above 1,000bps imply limited market access and potential difficulties refinancing external obligations.
Source: JP Morgan (via Haver). Frequency: Daily (6 April 2022)
Real effective exchange rate (vs 10-year average): Proxies exchange rate over/undervaluation. In lieu of detailed assessment of equilibrium REER (see here and here for detailed methodology and estimates).
Source: Bruegel (via Haver). Frequency: Monthly (March 2022). Note: Based on CPI differential with 38 largest trading partners
Current account balance / GDP (2022): Projected external funding needs (sources) arising from the current account deficit (surplus) as % of GDP.
Source: IMF April 2022 WEO. Frequency: Annual (2022)
Current account gap / GDP (2022-26): Difference between “cyclically adjusted CA” (proxied by the projected 2023-27 average, without cyclical adjustment) and the “CA norm” (proxied by 2010-19 average). Large deviations between the cyclically adjusted CA and CA norm point to external imbalances that must be resolved through BoP consolidation or exchange rate devaluation (see here and here for detailed methodology and estimates).
Source: IMF April 2022 WEO. Frequency: Annual (2010-27)
GEFR / reserves: Projected gross external financing requirement for 2022 derived by adding (subtracting) the current account deficit (surplus) to (from) estimated amortizations of medium and long-term (MLT) external debt, divided by gross FX reserves. Some sources exclude short-term external debt in their GEFR calculation, but we exclude due to data limitations.
Sources: IMF April 2022 WEO for current account and World Bank IDS DataBank for amortisations. Frequency: Annual (2022)
NIIP / reserves: Net international investment position (foreign assets less foreign liabilities). Proxy for risk of capital flight. We prefer non-resident holdings of domestic government debt or portfolio investment liabilities, but use this as a proxy due to data limitations.
Source: IMF International Investment Position. Frequency: Annually and quarterly (mixed)