By Phillip Torres, Global Co-head of Emerging Markets & Laura Fried, Research Analyst
As sovereign bond investors, we are primarily concerned with two types of credit scenarios: First, will the country default before the bonds we own mature—typically a 5-to-10-year time horizon—and second, what will the credit spread be next quarter? Assessing the probabilities of these scenarios is an inherently empirical exercise. For spreads, we have a long, robust history of mark-to-market bond prices. But for default rates, the data is relatively sparse.
It turns out that countries just don't default all that often. From 1990, approximately the time when emerging market (EM) debt was generally recognized as a standalone asset class, through 2017, there were only 17 true defaults on rated countries.1 That's roughly one default every 18 months so the sample size to study is rather small. Furthermore, estimating the transition to default appears to be much more of an art than science. Among those countries whose bonds defaulted, the average credit rating three years prior to a default was low BB; there were no CCC-and-below rated countries and only few low B-rated. Our insight here is that ratings alone provide inadequate information about medium-term default risks and can be misleading as predictors of spreads.
We are mark-to-market sensitive investors. As such, we are inclined to try to understand what credit spreads are likely to be over the next few quarters, instead of ignoring the path of spreads on the way to maturity or default. In our analysis of future credit spreads, we find that the accuracy is dramatically improved by identifying the region in which the country is located, as Exhibit 1 illustrates.
If investors were agnostic to region and just looked at credit ratings, they would treat a BB in Asia the same as Africa. In that case, in our analysis on rating and region, the regional effects should be insignificant (i.e., there is only one intercept, not five different ones). That they show up significantly and consistently, over long periods of time means that after controlling for credit rating, region plays a role in explaining spread.
There are some interesting questions that we have been asking ourselves about this insight—specifically, why should an identical credit and bond trade at a higher spread in Africa or Latin America than it would in Eastern Europe or Asia?
Exhibit 1: Spreads for identical credit risk vary notably by region*
*For illustrative purposes only. Source: Bloomberg, Aegon Asset Management US.As of November 30, 2017.
Any place like home
One possibility is that emerging markets investors, like all investors, suffer from home bias. It is no secret that investors are more cautious of the unknown. Emerging markets investors may do their due diligence with country visits, shaking the hands of government officials, and spending time on the ground gathering cultural anecdotes to eventually grow comfortable with the country. However, investors continue to gravitate toward the familiar.
The same holds true regarding the proximity of the foreign land to the investor. Those emerging markets in closer proximity to the investor may feel less risky. For instance, it may be easier for a US-based fund manager to gain cultural comfort with Latin America. It may be easier for London funds to gain cultural comfort with Eastern Europe than venturing further away to Latin America or Africa. And, it may be easier for Singaporean funds to gain cultural comfort with other Asian credits.
Nice house in a bad neighborhood
Another possible explanation is something we describe as the nice house in a bad neighborhood—bad house in good neighborhood phenomena. For example, countries like the Philippines experience positive spillover because they are situated in a stable, prosperous region with strong trading partners. Conversely, countries like Ethiopia are likely to experience negative spillover effects from surrounding countries in the form of instability and poor trading partners due in part to their geographic location. Further, during times of acute credit stress neighboring countries sometimes call a stronger neighbor for help (e.g., Portugal and Germany, Dubai and Abu Dhabi).
Governance and trust
Lastly, is it just that wealth and governance tend to be correlated, as Exhibit 2 illustrates, and investors can more easily believe the data and policy promises of an administrator from a wealthy region like Eastern Europe than a poorer one like Latin America? There are plenty of countries where we simply demand a higher risk premium because we do not believe, wholeheartedly, the veracity of the government's claims. We tend to put much more confidence in those countries that have a legacy of strong and capable institutions that could be trusted to produce quality reports and authenticated data.
Exhibit 2: The Correlation of Good Governance and Strong Economic Growth
Source: IMF and World Bank
As mark-to-market sensitive investors, we are interested in both default risk and the path of credit spreads. Historically, credit ratings have been poor indicators of potential default. To sufficiently consider the numerous variables that can affect future credit spreads and understand the credit quality of a particular country requires an exhaustive approach to credit analysis. Therefore, we spend considerable effort trying to understand the credit quality of the country given the veracity of the data we are using and relevant cultural/political context. We are mindful in our approach when considering our analysis and we use a variety of analytical techniques to help eliminate home bias. Even after all of these steps, we must take care not to allow ourselves to be influenced in ways that would hinder an unbiased analysis. By combining rigorous data analytics and qualitative contextual thinking, we aim to accurately predict future credit spreads.
1We define true defaults as credit events where the ratio of the present value of cash flows received as a result of the distressed exchange versus those initially promised, discounted using yield to maturity immediately prior to default are greater than 90%. The database of defaults is a compilation of recorded defaults from Moodys, S&P, and This Time is Different (Reinhart, Carmen M., and Kenneth S. Rogoff. 2009. This time is different: eight centuries of financial folly. Princeton: Princeton University Press.)
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