Quantifying EM vulnerability to risk aversion from Russia-Ukraine conflict

The EM bond sell-off has continued as the Russia-Ukraine conflict heats up, with the EMBI Diversified spread widening by 80bps and down 5.8% in total return terms from 18 February to 2 March (or 59bps and -2.9%, respectively, on an unweighted basis excluding Russia, Ukraine, Belarus, Lebanon, and Venezuela). The conflict has so far impacted hard currency EM bonds across three major channels: Risk aversion, commodity prices, and direct Russia/Ukraine exposure (geographically, strategically, trade, investment, remittances, tourism, etc – see here for a more detailed look at these factors).

Indeed, aside from the obvious candidates (like Russia, Ukraine and Belarus), weak performers have primarily been commodity importers (Kenya, Egypt, Pakistan), higher-yielding names (Ghana, Ethiopia, Argentina, Suriname), or both (Sri Lanka, Tunisia – see here for a more detailed look at the impact of the conflict on Sri Lanka). This highlights the importance of the risk aversion and commodity channels, which have sparked concerns about the ability of certain countries to meet widening external funding needs in the event of a continued risk-off environment for EM (with EM bond issuance off to a subdued start this year).

Outperformers have largely been commodity exporters (Zambia, Qatar, Saudi Arabia, Brazil, UAE, Angola, Oman, Kuwait, South Africa). However, the positive impact of rising commodity prices has been more than offset by rising risk aversion for many exporters, with spreads widening since the outbreak of the conflict for all but 13 of 70 EMBI constituents. Spread compression has also been modest, averaging just 14bps across 12 countries whose spreads have narrowed (excl. Venezuela) versus a 75bps widening across 53 countries whose spreads have widened (excl. Russia, Ukraine, Belarus, and Lebanon).

While there are a number of ways to analyse which countries are most at risk from the conflict (see related reading), we update our external liquidity index (last updated in November), which focuses on reserve coverage and external financing requirements, to highlight which countries could be most vulnerable. While there is a myriad of factors at play, our index provides a framework to quantify and flag which countries might face the greatest risk of a balance of payments (BOP) crisis triggered by a continued rise in global risk aversion.

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). The appendix provides a detailed explanation of each indicator:

Note: To request access to all the data from our Sovereign 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. We find that the most vulnerable countries are Sri Lanka, Ethiopia, Mongolia, Bolivia, Tunisia, Pakistan, Kenya, Egypt, Laos, and Jamaica. On the other hand, the least vulnerable countries are Iraq, Peru, Uruguay, Brazil, Namibia, Malaysia, Turkey, Colombia, and Azerbaijan.

Russia also scores well based on pre-invasion parameters, but this resilience is overstated given extensive Western sanctions which inter alia limits the central bank’s access to its overseas reserves. For this reason, the external liquidity framework is not relevant for analysing vulnerability in Russia (or in Ukraine and Belarus) under current circumstances.

Comparing each country's bond performance since the outbreak of the conflict to its external liquidity score, we find a strong relationship, as we would hope and expect to see. Stripping out Russia, Ukraine and Belarus, we find that the external liquidity score explains 40% of the variation in bond spreads from 18 February to 1 March at the 99% confidence interval, with a one standard deviation improvement in score leading to an 80bps improvement in performance over that period (the r-squared drops to 15% and coefficient to 40bps when excluding Sri Lanka, but the model retains its significance at the 98% confidence level).

Overall, the results show that countries with elevated pre-existing external vulnerabilities have been most severely impacted by the conflict. We think these countries will continue to underperform in the near term. However, if the underperformance of higher-yielding EM continues in the weeks ahead, it may present interesting entry points for countries where the sell-off exceeds fundamentals.

This would echo the 2020 Covid sell-off, where higher-yielding countries initially sold off the most before outperforming in the year that followed (for example, the EMBI Africa index fell by 28% in total return terms from 3 to 19 March versus 19% for EMBI Global and 11% for EMBI Asia, but then rose by 45% in the following 12 months versus 23% for EMBI Global and 12% for EMBI Asia).

Countries that the model flags as potentially having sold off in excess of fundamentals include Sri Lanka, Uruguay, Ghana, El Salvador, Argentina and Iraq. Conversely, countries that have outperformed model predictions are largely commodity exporters, likely justifying their outperformance, with the exception of Jamaica, Indonesia, Morocco and, to an extent, Mexico. Further country-level analysis is necessary to conclude whether under- or outperformance of model predictions is justified or a sign of under- or overvaluation (in Sri Lanka, for example, we think it is largely justified), which will be essential to identify which countries will likely out- or underperform in the weeks and months ahead and identify potential entry points.

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 non-linearities 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): 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

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 (2020) for ARA metric and monthly for total reserves

External principal payments / reserves: Principal payments due to external creditors in 2022 relative to gross FX reserves.

Source: World Bank International Debt Statistics. Frequency: Annual (2022)

Bond principal payments / reserves: Principal payments due to commercial external bondholders (incl. Eurobonds) in 2022 relative to gross FX reserves.

Source: World Bank International Debt Statistics. Frequency: Annual (2022)

External debt service / exports: Total external 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 for exports

External debt service / revenue: Total external debt service (principal + interest) relative to projected government revenue.

Source: World Bank International Debt Statistics for external debt service and IMF October 2021 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

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. 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 October 2021 WEO. Frequency: Annual (2022)

Current account gap / GDP (2022-26): Difference between “cyclically adjusted CA” (proxied by the projected 2022-26 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 October 2021 WEO. Frequency: Annual (2010-26)

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 October 2021 WEO for current account and World Bank IDS DataBank for amortizations. 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: Annual and quarterly

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