In 2007 sovereign spreads1 globally were at all-time lows. Thus only a few years ago market prices implied vanishingly small default probabilities for sovereign borrowers. Now this period looks more like the calm before the storm – a storm which has hit Greece with particular force and which continues to make headlines in Europe and in other parts of the world (most recently in Belize and Jamaica).

Important questions for investors and policymakers are which countries are most at risk of default and if there is anything that can be done to mitigate that risk. Hilscher and Nosbusch (2010) approach this question by building on the insights of the corporate bond pricing literature. We next discuss the motivation for that paper’s approach as well as its main findings.

In the seminal Merton (1974) model of corporate debt, a risky corporate bond is priced as a combination of a safe bond minus a put option on the value of the firm. The idea is simple but powerful. If the firm does well it repays its debt (just like the safe bond). If the firm does poorly, then bondholders are left with an asset which is worth less than what they originally lent the firm. In such a model a critically important input is the volatility of asset value: a high level of volatility means a high price of the option to default and a low price of the risky bond. Intuitively, if volatility is high, a large drop in asset value and a resulting default are more likely.

Empirically, the effect of volatility on borrowing costs is well understood in the case of corporate debt (see, for example, Campbell and Taksler 2003). However, in the case of sovereign debt this insight is relatively unexplored. An important difficulty in applying the corporate bond pricing intuition to the sovereign case is that, in the case of a country, there is no “asset value” which is easily measured or traded. Furthermore, in the case of default, creditors typically have no or very limited claims on a country’s assets. Thus default does not occur when some measure of “asset value” drops below the face value of debt but instead generally depends both on a country’s ability and its willingness to pay back creditors. Specifically, in order to pay hard currency2 debt, countries need to generate revenue in hard currency. For emerging market economies, this is typically achieved through exports. For a country with an important export sector we would therefore expect terms of trade3 – the relative price of exports to imports – to be an important macroeconomic determinant of default risk. For example, if the price of oil increases, a major oil exporter (e.g. Russia) has more funds to repay its external debt and is, all else equal, less likely to default.

It is an empirical question whether or not prices in the bond market accurately reflect the importance of changes in terms of trade as well as the volatility of terms of trade. The analysis in Hilscher and Nosbusch (2010) is based on a large data set4 that tracks emerging market external dollar denominated debt prices. Containing a total of 31 countries and data ranging from 1994 to 2007, they find that both factors are important. In particular, countries with stable export prices tend to have much lower borrowing costs than countries with unstable export prices. A one standard deviation change in terms of trade volatility is associated with a 164 basis point increase in spreads, which is roughly one half of the overall standard deviation in spreads. Consistent with the intuition in the Merton (1974) model of corporate debt, the volatility of macroeconomic fundamentals is an important determinant of borrowing costs.

In addition, the market does not view serial defaulters (borrowing an expression from Reinhart, Rogoff, and Savastano 2003) favorably. Unlike firms, countries do not disappear after they default and often return to the capital market to borrow. If a country has recently emerged from default, risk spreads on its external debt are higher.

In summary, macroeconomic fundamentals have significant explanatory power. Almost 60% of the variation in sovereign spreads is explained by a simple model containing only three sets of variables – macroeconomic fundamentals (based on terms of trade and the country’s default history), global variables, and the more commonly used ratios of debt to GDP and reserves to GDP.

One possible benchmark against which to compare this performance is to use credit ratings instead. This produces a noticeably smaller amount of explanatory power (a little over 50%). Related to this, country-level macroeconomic fundamentals contain information not reflected in credit ratings since adding such variables to a model containing only credit ratings significantly increases explanatory power (from about 50% to about 65%).

Another benchmark against which to compare model performance is the hypothesis that global factors are the main determinants of borrowing costs (e.g. Longstaff, Pan, Pedersen, Singleton 2007). In fact, global factors alone explain only 13% of variation in spreads across countries and over time, meaning that it is not only global factors, such as changes in overall risk and risk aversion, that matter. Some countries are inherently more risky and investors demand appropriate compensation in the form of higher spreads.

The country-level variables do not only explain variation in spreads, they also predict default.

Using a default forecasting model, it is instructive to ask how much of the variation in spreads can be explained by variation in default probabilities. Spreads can be thought of as containing three components, a default probability component that is related to the possibility of not being paid back in full; a component that captures the risk of default occurring during bad times; and compensation for the potentially low liquidity of sovereign debt. Using predicted default probabilities from the forecasting model it is possible to calculate the default component of the spread. Variation in this benchmark spread (together with global variables) explains almost 50% of the total empirical variation in spreads.

The source of the risk captured by terms of trade volatility can be traced to the often concentrated export sectors of emerging market countries. Many of these countries export mainly commodities with volatile prices determined in world markets. It is therefore possible to proxy for terms of trade volatility by using the price volatility of each country’s basket of commodity exports. Importantly, the results are robust to such a change in estimation strategy (specifically an instrumental variables approach).

What are the implications of these results? First, they demonstrate that volatility of fundamentals is a real and economically large determinant of country default and borrowing costs. Second, market prices reflect real variation in default probabilities and are not driven solely by changes in sentiment. Higher default probabilities tend to go hand in hand with higher spreads.

What do the findings mean for policymakers? First, the results suggest that it is possible to identify the source(s) of elevated risk, and, second, that it may be possible to mitigate these risks. Companies routinely trade in derivatives markets in order to hedge their exposures to volatile commodity prices (for example, airlines may hedge their exposure to changes in the oil price). Similarly, countries might be able to reduce their exposure to volatile commodity prices by trading in the same derivatives markets. Indeed Caballero (2003) and Shiller (2003), among others, have suggested such a possibility.

Pr. Jens Hilscher (Brandeis University)

Jens Hilscher is an Associate Professor of Finance at the Brandeis International Business School, which he joined in 2005. In 2008 he joined Kamakura Corporation of America as Senior Research Fellow. His research investigates the risks of corporate and sovereign default, the need to design governance structures that mitigate risk, and inefficiencies in financial markets. He received a teaching award in 2009 and in 2012, together with John Y. Campbell and Jan Szilagyi, was awarded the Harry M. Markowitz Journal of Investment Management Best Paper Award. Hilscher holds a B.Sc. and M.Sc. in Economics from the London School of Economics and a Ph.D. in Economics from Harvard University. 

RESEARCH INTERESTS : Asset Pricing / Behavioral Finance / Corporate Finance / Credit Risk / Sovereign Debt 

INSTITUTIONS : Brandeis University / Kamakura Corporation

REFERENCE PAPER : Determinants of sovereign risk: Macroeconomics fundamentals and the pricing of sovereign debt  
J. Hilscher and Y. Nosbusch. 
Review of Finance, Vol. 14, No. 2, 235-262, April 2010.


Dr. Yves Nosbusch (London School of Economics)

Yves Nosbusch is the Chief Economist of BGL BNP Paribas in Luxembourg. Before taking up his current position, he was a Lecturer in Finance at the London School of Economics where he continues to teach a course on financial risk analysis to M.Sc. students. His primary research interests lie in the areas of public debt (sovereign risk, optimal maturity structure) and pension systems. He holds a Ph.D. in Economics from Harvard University and M.Sc. and B.Sc. degrees in Econometrics and Mathematical Economics from the London School of Economics. He was a member of the Council of the Central Bank of Luxembourg from 2010 to 2012.

RESEARCH INTERESTS : Credit Risk / Empirical Asset Pricing / Optimal Maturity Structure / Pension Systems / Sovereign Debt

INSTITUTIONS : BGL BNP Paribas / London School of Economics 

REFERENCE PAPER : Determinants of sovereign risk: Macroeconomics fundamentals and the pricing of sovereign debt  
J. Hilscher and Y. Nosbusch. 
Review of Finance, Vol. 14, No. 2, 235-262, April 2010.


  1. The difference between yields to maturity of bonds issued by a country and the corresponding benchmark for that currency, e.g. U.S. government bonds in the case of USD denominated debt.
  2. Hard currency refers to liquid and widely-traded currencies such as, for example, the USD, the Euro, or the Japanese Yen.
  3. The ratio of the price of exported goods over the price of imported goods.
  4. The empirical analysis is based on JP Morgan Emerging Market Bond Index Global (EMBI Global) data. This data tracks large (above 500 million US dollar) bond issues that have daily market prices and measures bond spreads over U.S. Treasuries.
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