Aurora Borealis
1 December 2015 CFA Institute Journal Review

A Quantitative Approach to Assessing Sovereign Default Risk in Resource-Rich Emerging Economies (Digest Summary)

  1. Sandra Krueger, CFA

The assessment of sovereign debt default risk is not straightforward. Valuations of fundamental components of credit risk, such as asset values and equity prices, can be impossible to determine for sovereigns. Excessive external debt (nonresident debt) is often used as an indication of financial health, but safe ratios are difficult to define. The authors propose an approach for assessing sovereign default risk in resource-rich countries and show how optimal debt ratios can define a “distance from default” indicator.

What’s Inside?

Valuing sovereign credit default risk is an imprecise discipline. The authors examine an approach that is similar to Moody’s KMV model and apply it to sovereign issues. The countries studied are intentionally limited to countries that borrowed heavily after a resource discovery that subsequently led to an economic boom. The authors investigate the mechanics of different debt crises beginning in the 1970s and how these events affected the probability of default. They further analyze external debt using foreign assets and liabilities, which are obtained from the International Investment Position (IIP) data from the IMF.

How Is This Research Useful to Practitioners?

Most default risk models that have been proposed for corporations are unusable for sovereign issues. The calculation of optimal debt ratios can provide a starting point for the analysis of sovereigns.

Global bond portfolio managers and traders of credit derivatives may be able to use the authors’ credit risk metric as an additional risk management tool to identify countries for which sovereign default risk may begin to rise.

How Did the Authors Conduct This Research?

The authors focus their study on seven resource-rich countries: Argentina, Brazil, Mexico, Algeria, Nigeria, Indonesia, and Malaysia. They begin by analyzing the sustainability of external debt. As external debt rises, economic growth tends to decline.

The authors compare historical debt-to-GDP ratios with calculated net returns (return on assets minus real interest rates) in different countries. The optimal debt ratio is determined from the net return. They try to determine whether there is a higher default probability associated with large deviations from optimality. As productivity falls or interest rates increase, the vulnerability to a debt crisis increases. The authors find that three optimal ratios are useful in evaluating default probability:

  1. Optimal ratio of external debt to net worth
  2. Optimal ratio of external debt to GDP
  3. Optimal ratio of current account to GDP

The authors use these ratios to modify Moody’s KMV model for sovereign issues. Actual and optimal ratios for all seven countries are calculated historically and debt crises highlighted. The path of these ratios is studied for early warning signals. The research results indicate that there were early warning signals of excess debt for Mexico, Brazil, and Argentina during 1979 and 1994. Prior to the Asian financial crisis, there were early warning signals of excessive debt for only Thailand and South Korea. Although Indonesia defaulted in 1998, it and other Asian countries were most likely affected by contagion. Policymakers are encouraged to track these ratios in an attempt to keep debt, GDP, and current account levels steady at a point that is unlikely to contribute to future crises.      

Abstractor’s Viewpoint

Sovereign credit analysis is challenging because it is impossible to remove the political factors in valuing a sovereign issue. Additionally, there is frequently little data available. The authors’ risk metric and use of the GDP growth rate in calculating debt ratios appears to act as a debt crisis warning signal. The authors point out that some neighboring countries that did not show any excess debt are affected by the crisis contagion. Perhaps future research could be directed to predicting which neighboring countries will suffer contagion and which will not.

We’re using cookies, but you can turn them off in Privacy Settings.  Otherwise, you are agreeing to our use of cookies.  Accepting cookies does not mean that we are collecting personal data. Learn more in our Privacy Policy.