شاخص ریسک کشور جدید برای بازارهای نوظهور: یک روش تسلط تصادفی

An optimal weighting scheme is proposed to construct economic, political and financial risk indices in emerging markets using an approach that relies on consistent tests for stochastic dominance efficiency. These tests are considered for a given risk index with respect to all possible indices constructed from a set of individual risk factors. The test statistics and the estimators are computed using mixed integer programming methods. We derive an economic, political and financial risk ranking of emerging countries. Finally, an overall risk index is constructed. One main result is that the financial risk is the leading contributor to sovereign risk in emerging markets followed by the economic and political risks.

There is a growing awareness that sovereign debt crises can quickly mushroom — as events in a number of emerging countries in the late 1990s have shown (Sturzenegger and Zettelmeyer, 2006) and, more recently, as a consequence of the global economic and financial crises, that affected public debt and sovereign risk, hitting developed and emerging countries with varying intensity and persistence. It is also a common understanding that emerging economies are prone to financial crises and some of the major financial crises affecting emerging markets in recent years have been linked to risky external and domestic debt compositions, rollover risks, contingent interest payments and the poor credibility of monetary and fiscal policies (see Eichengreen and Hausmann, 2005, Hausmann and Panizza, 2003, Jeanne, 2003 and Zettelmeyer and Jeanne, 2002). Rating downgrades were relatively rare until the 1990s, and when they occurred, were of modest size and manageable. Nowadays, the credit quality of the sovereign sector is by far more heterogeneous and unstable.1 Also in view of the globalized dimension of economic and financial markets, country risk assessment has become a more urgent matter now than ever. And yet, there is no good understanding of the sources of vulnerability and of the determinants of country risk. Whether the problem is a weak banking sector, an excessive public or private sector external burden, some structural impediment to growth, lack of transparency of a country's political institutions – just to mention a few of the determinants considered by most providers of risk ratings – a satisfactory comprehensive measure of country risk is still to be found. A good index of country risk is also crucial for strengthening the policy response towards economic improvement and sovereign creditworthiness. The objective of our paper is to derive a new country risk index for emerging markets that outperforms the most common existing sovereign risk indicators and, at the same time, allows us to disentangle the contributions of economic, political and financial risk factors. There are many services measuring country risk. Among the foremost providers of risk ratings, there are: the International Country Risk Guide (ICRG); the Institutional Investors (II); the Business Environment Risk Intelligence (BERI); the Economist Intelligence Unit (EIU); Euromoney; and services of the major rating agencies, that is, Standard and Poor's; Moody's and Fitch. The synopsis in Fig. 1 compares these indices. Full-size image (139 K) Fig. 1. Country risk indices. Figure options All the above-mentioned indices are based on arbitrary weighting of the relevant variables and most of them share the conventional wisdom that political risk is the key determinant. Relatively little research has focused on the construction of a country risk index. Erb et al. (1999) explained this lack of academic work, especially for emerging markets, because of the short time series that exist, making it very difficult to produce an accurate evaluation of the characteristics of the market. To the best of our knowledge, the only work analyzing some measures of country risk is Erb et al. (1996). They explore the political, economic, financial and composite risk indices from ICRG and the II country credit ratings and provide a comparison of S&P's and Moody's ratings. They find out that rank order correlation is higher between S&P's or Moody's rating and the ICRG financial risk index. Moreover, through the construction of a portfolio of countries that experienced a decrease in risk rating and a portfolio of countries that experienced an increase in risk rating, they investigate whether the risk indices contain information about future expected returns. They find that the financial risk index contains the most information about future expected returns and the political risk contains the least. A broad literature has studied which factors determine or affect a country's “ability” and “willingness to pay”. A first group of contributions investigates the determinants of sovereign credit ratings. In their seminal paper, Cantor and Packer (1996a) use regression analysis to measure the relative significance of eight variables that are listed in Moody's and Standard and Poor's reports and show that GDP growth, per capita income, external debt, inflation and indicator variables for economic development and default history are the main determinants of the ratings issued by S&P and Moody's. Afonso (2003) updates Cantor and Packer (1996a) and finds analogous outcomes. A further updating is in Afonso et al. (2011b), where a distinction between short- and long-run determinants of sovereign ratings is introduced. They show that the level of GDP per capita, real GDP growth, public debt level and the government balance sheet have a consistent short-run impact, while the level of external debt and external reserves together with government effectiveness is important long-run determinants. Another set of works examines the relationship between spreads and sovereign ratings. Cantor and Packer (1996b) find that agency disagreements over sovereign ratings are quite common and that the rank orderings of sovereign risks implied by market yields frequently differ from the ratings assigned by the agencies. In particular, financial markets seem to be more pessimistic about sovereign credit risk than are the rating agencies, meaning that rating agencies undervalue the perception of financial risk. Such differences of opinion appear to be most extreme for below investment-grade countries. An updated comparison of sovereign ratings is in Flandreau et al. (2009) and in Afonso et al. (2011a), where possible spillover effects from lower rated countries to higher rated countries are also considered. A recent work, assessing the effect of sovereign credit rating announcements on sovereign CDS spreads for emerging markets, is Ismailescu and Kazemi (2010). There is a large empirical literature based on regression analyses that treat a debt crisis as the dependent variable and a number of economic, political and institutional variables as independent variables. The latter typically include solvency indicators such as the ratio of debt to GDP, GDP growth, the real exchange rate, liquidity indicators and the level of international reserves. Several recent papers, such as Reinhart (2002), Catao and Sutton (2002), Reinhart et al. (2003), Van Rijckeghem and Weder (2004), Kruger and Messmacher (2004), and Catao and Kapur (2004), have included other institutional and political variables, debt history, financing needs indicators and macroeconomic volatility. Some studies investigate the effects of macroeconomic fundamentals on sovereign credit spreads, under the view that a higher yield spread reflects higher risk. Hilscher and Nosbusch (2010) focus on terms of trade for emerging markets and examine the relative importance of country-specific and global factors. Hui and Lo (2002) develop a model to value defaultable bonds and focus on foreign exchange rates as the main variable to track the credit spreads in some emerging markets. Eichengreen and Mody (1998) conclude that changes in market sentiment, not obviously related to fundamentals, have moved the markets by large amounts. The results from these studies are in keeping with most theories about “the ability to pay” and the “willingness to pay”, and stress that sovereign debt risk crises tend to occur more often the higher the debt to GDP ratio, the lower growth, the lower reserves, the higher the financing needs and the worse the quality of the institutions. A serious shortcoming is that the construction of the above country risk measures, as in the case of the separate analysis of single attributes, ignores the association among the various risk factors. In this paper we will follow an approach for the construction of aggregate indices for economic, political and financial risks in emerging countries based on stochastic dominance (SD hereafter) analysis. Constructing an optimal country risk index based on SD analysis has advantages since it provides an efficient index resulting from the least variable combination of risk factors that offers the maximum level of risk environment over time for each country or group of countries. Moreover, relatively large data sets are available, so that nonparametric analysis can let the data “peak for themselves”. The optimality of the index refers to the fact that it gives the greatest value of risk environment for economic, political and financial sectors in emerging countries. In other words, we will construct an index with those weights given to each risk factor in each sub-index that will make it stochastically dominate all other competitor indices. Mostly, stochastic dominance comparisons are made pair wise in the literature. Barrett and Donald (2003) developed pair wise stochastic dominance comparisons that relied on Kolmogorov–Smirnov type tests developed within a consistent testing environment. This offers a generalization to Anderson (1996), Beach and Davidson (1983), and Davidson and Duclos (2000) who have looked at the second order stochastic dominance using tests that rely on pairwise comparisons made at a fixed number of arbitrarily chosen points. This is not a desirable feature since it introduces the possibility of a test inconsistency. Linton et al. (2005) propose a subsampling method which can deal with both dependent samples and dependent observations within samples. This is appropriate for conducting SD analysis with country panel data. In this context, comparisons were available for pairs where one can compare risk levels in one year relative to previous years and conclude whether there is a higher presence of risk or not. Gonzalo and Olmo (2010) introduce nonparametric consistent tests of conditional stochastic dominance of arbitrary order in a dynamic setting. Both Linton et al. (2005) and Gonzalo and Olmo (2010) propose consistent SD tests, which could be applied also to sovereign risk analysis; however, although both tests allow for comparison over time and among different risk factors, they are restricted to pairwise comparisons only. Lately, multi-variate (multidimensional) comparisons have become more popular. In an application to optimal portfolio construction in finance, Scaillet and Topaloglou (2010), hereafter ST, use SD efficiency tests that can compare a given portfolio with an optimal diversified portfolio constructed from a set of assets. In a related paper, Pinar et al. (forthcoming) use a similar approach to construct an optimal Human Development Index. In this paper we follow the same methodology, using the set of risk variables (in our case economic, political, and financial risk factors for each respective index) to construct the optimal economic, political, and financial risk indices that do not rely on arbitrary weights as rating institutions do. An arbitrary weight to each risk factor would assign a predetermined or “perceived” importance of it. It is possible that the importance of some risk factors may change over time and may be different for different groups of countries. Some risk factors which are included in the overall risk index may become obsolete and some other risk factors which are excluded from the analysis may gain importance over time. In other words, as time passes, the evolution of risk factors for different groups of countries can vary over time (e.g., a new set of country coverage may add new characteristics of risk to the existing ones, and/or risk factors for any given cross section of countries may change over time). Therefore, weights assigned to each risk factor may have to be re-estimated over time.2 The main contribution of this paper is to derive an optimal country risk index based on SD efficiency analysis. We use consistent SD efficiency analysis to determine the optimal weights assessing the relative importance of the risk factors for emerging market economies. By using SD efficiency analysis, we test the optimality of the equally-weighted risk index with respect to all possible weighting combinations of risk factors and obtain the optimal risk index by maximizing the cumulative risk difference between a given index (i.e. equally-weighted risk index) and the alternative one. Therefore, the optimal risk index offers the riskiest factors that are persistently high for emerging market economies rather than predetermined arbitrary weights, which are commonly used by rating institutions. The index we obtain will offer the maximum level of risk environment in emerging markets for a given probability level and also be the least volatile over time among its set of competitors. By weighting each risk factor differently, we find the riskiest economic, political and financial environments for a larger number of countries over time than under the arbitrary weighted risk measures offered by rating institutions. We also find the weighting scheme of each sub-index (i.e. economic, political and financial risks) which offers the overall riskiest environment for the emerging economies. Our findings have relevant policy implications too. The optimal economic, political, financial and overall risk indices highlight the riskiest factors which are persistently high for the majority of the emerging countries over time. This implies that an emerging country should adjust its agenda consequently in order to keep sovereign risk under control. We also find that the financial risk index is the main contributor to overall risk for emerging markets. Thus, our analysis is able to capture the growing expansion of the financial sector in emerging countries and the leading role of monetary and financial institutions. Finally, our paper contributes to the current debate on the reliability of the rating assignment by S&P and its sister rating agencies. We show that our index is not affected by their documented reluctancy to change the rating class and asymmetry between the upgrading and downgrading moves. The remainder of the paper is as follows. In Section 2 we examine the main framework of analysis, define the notions of stochastic dominance and discuss the general hypothesis for stochastic dominance of any order. In Section 3 the mathematical formulation of the tests is presented. Section 4 develops the empirical analysis. We first look at the data used for economic, political and financial indices and offer descriptive statistics and use the ST methodology to find the optimal index for economic, political, financial and overall risk environment for emerging markets. Then we rank the countries for each sub-index and the overall risk environment. A comparative analysis with the rankings of the main rating agencies is offered too. Finally, Section 5 concludes. In the appendix we describe practical ways to compute p-values for testing stochastic dominance efficiency at any order by looking at block bootstrap methods and discuss the theoretical justification for these methods.

This paper uses stochastic dominance efficiency tests at any order for time dependent data. We study tests for stochastic dominance efficiency of a given index with respect to all possible indices constructed from a set of individual components. We proceed to test whether stochastic dominance efficiency justifies the use of the arbitrarily-weighted risk indices obtained by rating agencies for the sovereign risk assessment: economic, political and financial risks. The results from the empirical analysis indicate that equally weighting risk factors in each sub-index does not produce an optimal index in the SD efficiency sense. We can construct many alternative indices that dominate the equally weighted sub-index risk value and assign a riskier environment to each emerging market country. Moreover, we construct an overall risk index and the results show that the financial risk is the main contributor to the overall sovereign risk environment in emerging markets followed by economic and political risks. The implications of these results are important. We propose the riskiest factors in economic, political and financial terms for emerging countries. Higher sovereign risk among emerging markets over time can be mainly attributed to financial factors and as such reducing overall risk for a particular country would imply improvements of this country's financial institutions. Secondly, an emerging market country should have high growth rates, export more and have a positive budget balance to be less subject to economic risk; decrease the level of corruption, have fair legal systems and improve its institutional effectiveness in order to reach a better political environment; increase liquidity and decrease banking sector risk to achieve a better financial institutional environment. We should mention that the upper and lower bounds of each index may change over time; therefore, we should mention that the weighting scheme assigned to each index may change over time. Moreover, there may be a major improvement and/or deterioration of risk factors in each sub-index over time for emerging countries. Furthermore, one may expect that there may be some other risk factors found to be important in the future not captured in the current analysis. Since the optimal weighting scheme in the construction of the each sub-index changes i) as the bounds change, ii) if there is major improvement or deterioration of some risk factors for emerging countries and iii) with the inclusion of some other risk factors in the analysis, the stochastic dominance efficiency of the optimal risk indices should be tested periodically. As a further research, one could employ other consistent SD pairwise tests, like in Linton et al. (2005) and Gonzalo and Olmo (2010), to determine whether one year has riskier outcomes than another year and/or whether some risk indicators are riskier than others in a given year using a pairwise comparison. It is also possible to determine the riskiest time periods for emerging markets and further analyze the main factors that precipitated this high risk environment. Another possible extension is to employ SD efficiency tests to analyze shorter span data to obtain optimal weights in order to refine the forecast of future sovereign crises. Finally, our methodology could be fruitfully applied to other group of countries, and in particular to mature economies where the risk of sovereign debt crises has increased dramatically.