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External liquidity scorecard: An early warning system for external distress

  • We present an external liquidity scorecard for 40 emerging and frontier markets to use as an early warning system

  • The most vulnerable countries are Sri Lanka, Laos, Bahrain, Ethiopia, Kenya, Tunisia, Jamaica, and Costa Rica

  • The least vulnerable countries are Russia, Brazil, Peru, South Africa, Vietnam, Argentina, Mexico, and Malaysia

External liquidity scorecard: An early warning system for external distress
Tellimer Research
11 January 2021
Published byTellimer Research

The initial shock of the Covid-19 pandemic has faded, but the recovery will be slow as many countries face second and third waves before the vaccine is rolled out in scale. And, for many emerging markets, this year begins with a larger debt overhang and smaller buffers than the last. So while last year saw a record-breaking six sovereign bond defaults, vulnerabilities will still be heightened over the coming year as the full impact of the Covid crisis makes itself apparent.

With that in mind, we kick off the year with a top-down look at which emerging markets are at the greatest risk of an external liquidity or balance of payments crisis. Sourcing indicators from centralised sources like the IMF and World Bank for availability and comparability across countries, we have developed an “external liquidity scorecard” for 40 emerging and frontier markets that uses 11 variables that we have chosen subjectively for their predictive power or information content (we exclude countries that are already in default, as it is meant to be an early warning signal).

External liquidity scorecard

External liquidity ranking

Each indicator is detailed in the Appendix, as well as some additional ones that were only available for a limited subset of countries. The first table shows the data in raw form, and the second takes that data and assigns a numerical rank from 1-40 (with higher numbers corresponding to greater vulnerability). It then provides an average rank for each country based on the simple average of all available indicators.

The resulting output is a quick and dirty way to quantify the risk of an external debt or balance of payments crisis for each country and to enable cross-country comparisons.

Results and investment implications

We find that the most vulnerable countries are Sri Lanka, Laos, Bahrain, Ethiopia, Kenya, Tunisia, Jamaica and Costa Rica. The least vulnerable countries are Russia, Brazil, Peru, South Africa, Vietnam, Argentina, Mexico and Malaysia.

Moreover, a simple scatter plot shows that there is a positive relationship between the overall liquidity ranking and the country risk premium (measured by the EMBI), as we should expect. Adding a line of best fit (statistically significant at the 95% confidence level with an R2 = 13.7%), we can use this model to make inferences about bond valuations.

On that basis, Sri Lanka, Argentina, Ecuador, Angola and Iraq all stand out as notably undervalued, despite elevated external vulnerabilities. We have outstanding Buy recommendations on Sri Lanka, Angola and Iraq, while Ecuador and Argentina primarily score highly because of their favourable post-restructuring debt profile in 2021. Meanwhile, Serbia, Croatia, Bahrain, Jamaica and Uruguay appear to be the most overvalued credits, although idiosyncratic factors, like Bahrain’s access to GCC financing for example, must also be considered.

External liquidity rank versus EMBI spread

Methodological issues

Although 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. The simple approach of ordinally ranking each indicator 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 and oftentimes adding some useful context. Nor have we backtested the model and assessed its predictive power; any model is likely to give false signals (Type 1 and Type 2 errors).

Moreover, we omit other indicators that might be a cause of or signal distress, and exclude 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 of external stress. Data availability and the vintage of the available data is another challenge, although 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).

In Appendix A, we explain each indicator and its source. In Appendix B, we discuss additional indicators that may add useful colour and context but were not readily available for enough countries to include on the scorecard. In Appendix C, we outline some of the raw data that went into the calculations in the scorecard. And finally, in Appendix D we provide solvency indicators (bearing in mind that this scorecard is primarily aimed at flagging external liquidity stress). We welcome feedback from our readers on methodology and coverage and remain available to answer any questions. 

Appendix A: Data explanations and sources

Gross FX reserves (months of imports): Common international reserve benchmark, calculated in this instance by dividing gross FX reserve holdings by the trailing 12-month average of monthly imports (rather than the forward-looking 12-month estimate of goods and services imports, for which data was lacking). Net reserves may be a more useful indicator, subject to availability.  

Source: IMF International Financial Statistics (via Bloomberg) for reserves and IMF Direction of Trade Statistics (via Bloomberg) for imports. Frequency: Monthly (varies for reserves and September 2020 for imports)

Short-term external debt/reserves: Short-term external debt on remaining maturity basis in 2021 relative to reserves (see above).

Source: IMF Assessing Reserve Adequacy DataMapper (via Bloomberg). Frequency: Annual (2021 proj.)

Eurobond debt service/reserves: Eurobond principal and interest payments due in 2021, according to Bloomberg’s DDIS function.

Source: Bloomberg DDIS. Frequency: Annual (2021)

External debt service/exports: The external debt service ratio - total external debt service (principal + interest) relative to 12-month trailing exports of goods. Exports of good and 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 (2021) for external debt service and monthly (September 2020) for exports

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

Source: World Bank International Debt Statistics for external debt service and IMF October 2020 WEO for government revenue. Frequency: Annual (2021 proj.)

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 (5 January 2021)

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 (November 2020). Notes: Based on CPI differential with 38 largest trading partners

Current account balance/GDP (2021): Projected external funding needs (sources) arising from the current account deficit (surplus).

Source: IMF October 2020 WEO. Frequency: Annual (2021 proj.)

Current account gap/GDP (2021-25): Difference between “cyclically adjusted CA” (proxied by the projected 2021-25 average, without cyclical adjustment) and the “CA norm” (proxied by 2015-19 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 2020 WEO. Frequency: Annual (2021 proj.)

Gross external financing requirement/reserves: Estimate of external funding needs for 2021 derived by adding (subtracting) the current account deficit (surplus) to (from) estimated amortisations of medium- and long-term (MLT) external debt. Some sources exclude short-term external debt in their GEFR calculation, but we deliberately exclude it because it reduces the sample size.  

Sources: IMF October 2020 WEO for current account and World Bank IDS DataBank for amortizations. Frequency: Annual (2021 proj.)

Net international investment position/reserves: Net foreign assets less net foreign liabilities. Proxy for risk of capital flight. Used in lieu of non-resident holdings of domestic government debt (see Appendix B).

Source: IMF International Investment Position. Frequency: Annually and quarterly (mixed)

Appendix B: Additional indicators

Non-resident holdings of domestic government debt: In addition to large levels of external debt, balance of payments risk is heightened for countries with a large proportion of domestic debt held by foreigners. This is captured within the NIIP/GDP indicator, but we consider non-resident holdings of domestic government debt to be a more useful indicator. The average across the 15 countries for which data was available is 19%, ranging from 0.1% in Sri Lanka to 53.4% in Uruguay.

Non-resident holdings of domestic government debt

FX deposits as % of total deposits (dollarization rate): High levels of dollarization signal a lack of confidence in the currency by domestic depositors. The average across the 13 countries for which data was available is 38%, ranging from 0.8% in the Dominican Republic to 83.4% in Uruguay.

Dollarization rate (%)

Foreign exchange swaps: Some countries use FX swaps to inflate reserves, rendering their reserve figures misleading. This is especially relevant for Turkey (which has FX swaps of US$43.7bn as of 6 January) and Pakistan (which we estimate at US$5.75bn as of June). For these countries (and possibly others like them), reserve data should be interpreted with a degree of caution. 

Appendix C: Raw data and supplementary indicators

Raw data and supplementary indicators

Appendix D: Debt solvency indicators

Debt solvency indicators