Over the last years, worldwide financial market instability has shaken confidence in global economies. Global financial crisis and changes in sovereign debts ratings have affected the Latin American financial markets and their economies. However, Latin American’s relative resilience to the more acute rise in risk seen in other regions like Europe during last years is offering investors new options for improving risk-return trade-offs. Therefore, forecasting the future of economic situation involves high levels of uncertainty. The Country Risk Score (CRS) represents a broadly used indicator to measure the current situation of a country regarding measures of economic, political, and financial risk in order to determine country risk ratings. In this contribution, we present a diffusion model to study the dynamics of the CRS in 18 Latin American countries which considers both the endogenous effect of each country policies and the contagion effect among them. The model predicts quite well the evolution of the CRS in the short term despite the economic and political instability. Furthermore, the model reproduces and forecasts a slight increasing trend, on average, in the CRS dynamics for almost all Latin American countries over the next months.
Worldwide financial market instability has shaken confidence in global economies. This loss of confidence has a strong influence on the capital flows [
In Latin America and until the first half of 2011, the South and Central American economies expanded at a high pace. However, since late 2011, this strong growth started to slow down. This growth was particularly strong in South America (Chile, Brazil, Peru, Colombia, Uruguay, Paraguay, Argentina, Bolivia, Venezuela, Ecuador, Guyana, Suriname, Trinidad, and Tobago) due to strong economic demand, better external financing conditions, and higher commodity exportation prices, whereas in Central America (Mexico, Panama, Costa Rica, El Salvador, Guatemala, Honduras, Nicaragua, and Belize), growth has been subdued but also accelerated due to the recovery of domestic demand and a stronger agricultural expansion in Mexico. On the other hand, most of the Caribbean economies (Dominican Republic, Haiti, or Cuba) growth remained weak.
Global financial crisis and changes in sovereign debts ratings and confidence have also affected the Latin American financial markets and their economies. In fact, experiences from former currency crashes (Argentina, Mexico, etc.) are fueling avid interest in the Eurozone crisis, with the corresponding cross-country contagion. Traditional concepts of risk, solvency, liquidity, or foreign investment grade allow us to understand this issue. This is the main reason why country risk ratings have become a topic of major concern for the international financial community over the last two decades. Kaminsky and Schmmkler state: “the effects of rating and outlook changes are stronger during crisis, in nontransparent economies, and in neighboring countries” [
The importance of country ratings is also underscored by the existence of several major country risk rating agencies [ Political risk Economic performance Debt indicators: Calculated using the following ratios from the World Bank’s Global Development Finance figures: total debt stocks to GNP (A), debt service to exports (B), and current account balance to GNP (C). Developing countries which do not report complete debt data get a score of zero. Structural assessments Access to bank finance/capital markets: Participants rate each country’s accessibility to international markets. Credit ratings: Nominal values are assigned to sovereign ratings from Moody’s, Standard and Poor’s, and Fitch IBCA.
Thus, CRS can represent a complete indicator of the current situation of a country regarding measures of economic, political, and financial risk in order to determine country risk ratings. In the case of Euromoney Agency, the overall (ECR) Euromoney Risk Score is obtained by assigning to the six categories introduced above the following weights: Three qualitative expert opinions: political risk (30% weighting), economic performance (30% weighting) and structural assessment (10% weighting), Three quantitative values: debt indicators (10% weighting), credit ratings (10% weighting), and access to bank finance/capital markets (10% weighting).
When talking about financial crises there is a lot of literature that takes into account several reasons for crises to appear in clusters [
This paper is organized as follows. Section
Scheme summarizing the techniques applied through this paper.
This section is addressed to construct and justify the mathematical model used to describe the dynamics of the CRS of the 18 most important Latin American countries (listed in Table
Clustering obtained by non-hierarchical clustering technique.
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Country name | Cluster |
|
Country name | Cluster |
---|---|---|---|---|---|
1 | Chile |
1 |
8 | Honduras | 3 |
9 | Ecuador | 3 | |||
10 | Nicaragua | 3 | |||
11 | Dominican Republic | 3 | |||
12 | Trinidad and Tobago | 3 | |||
| |||||
2 | Brazil | 2 | 13 | Costa Rica | 4 |
3 | Mexico | 2 | 14 | Paraguay | 4 |
4 | Peru | 2 | 15 | El Salvador | 4 |
5 | Colombia | 2 | 16 | Argentina | 4 |
6 | Uruguay | 2 | 17 | Bolivia | 4 |
7 | Panama | 2 | 18 | Venezuela | 4 |
Initial CRS data corresponding to February 6, 2012 (Euromoney, 2012 [
Country name | Initial CRS | Country name | Initial CRS |
---|---|---|---|
Chile | 75.07 |
Honduras | 37.95 |
Ecuador | 34.25 | ||
Nicaragua | 31.43 | ||
Dominican Rep. | 35.40 | ||
Trinidad Tobago | 51.59 | ||
| |||
Brazil | 62.76 | Costa Rica | 52.27 |
Mexico | 58.93 | Paraguay | 41.40 |
Peru | 55.76 | El Salvador | 42.73 |
Colombia | 59.61 | Argentina | 59.61 |
Uruguay | 50.61 | Bolivia | 35.38 |
Panama | 57.78 | Venezuela | 35.55 |
As we said previously, in finance and especially in country risk assessment, it is useful to group the different countries sharing similar economic characteristics. The clustering technique allows us to gather the different countries into homogeneous groups. Thus, before constructing our dynamic diffusion model, we have performed a clustering. As we will see later, an additional advantage of the clustering is the reduction of the number of model parameters to be estimated. In order to deal with this task, we have used the non-hierarchical clustering (also termed
The first cluster gathers Chile, the safest and most prosperous South American economy. It should be also remarked that it leads Latin American nations to human development, competitiveness, income per capita, globalization, economic freedom, and low perception of corruption [
In the following, for our dynamic diffusion model, we are going to assume that the obtained clustering does not change over the time. This hypothesis is reasonable because, as we said previously, we are going to predict CRS evolution in a short time and there will be very few countries that may move out from one cluster to another in the studied period. Furthermore, the obtained clusters represent quite accurately the current economic Latin American groups.
Once the clusters have been established, we propose a diffusion dynamic model to study the evolution of the CRS of each Latin American country. Diffusion dynamic models have been demonstrated to be powerful tools to study a wide range of applied problems in different areas including economics and its related fields [
For the sake of clarity in the model setting, we will identify each one of the 18 Latin American countries with the index
The term The factors responsible for the contagion effect are embedded in the
Taking into account the previous exposition that includes both autonomous and transmission behavior, we propose the following diffusion dynamic model, based on a coupled system of 18 nonlinear differential equations, one per country, to study the dynamic evolution of the CRS’s Latin American countries:
This section is divided into three subsections. The first one, is devoted to model parameters estimation. Since uncertainty and variability are the rules when dealing with modelling real problems, in the second subsection, we provide predictions by means of confidence intervals obtained using a cross-validation technique. In the third one, we validate and discuss the obtained results.
As we have previously pointed out, this subsection is firstly addressed to estimate the parameters of model (
The system of differential equations (
Estimation of the autonomous model parameters,
Country name |
|
Country name |
|
---|---|---|---|
Chile | 1.6844 |
Honduras | −0.0440 |
Ecuador | −0.5731 | ||
Nicaragua | −0.7551 | ||
Dominican Rep. | −0.4340 | ||
Trinidad Tobago | 1.3411 | ||
| |||
Brazil | 1.8554 | Costa Rica | 1.0763 |
Mexico | 1.2784 | Paraguay | −0.0203 |
Peru | 1.1126 | El Salvador | 0.4658 |
Colombia | 1.5382 | Argentina | −0.4093 |
Uruguay | 0.0696 | Bolivia | −0.5837 |
Panama | 1.2563 | Venezuela | −0.4590 |
Estimated values of the contagion model parameters. The value of model parameter
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|
|
|
|
---|---|---|---|---|
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0 | 0.01122 | 0.02525 | 0.01736 |
|
0.05222 | 0.00007 | 0.04198 | 0.07828 |
|
0.00047 | 0.00180 | 0.03809 | 0.04129 |
|
0.00004 | 0.00283 | 0.04903 | 0.04771 |
For the sake of clarity, in Figure
Representation of the estimation of the autonomous model parameters,
In this figure we use a grayscale to represent the level of contagion from cluster in column
CRS fitting for the 18 selected Latin American countries. The solid line is the model solution for the parameters of Tables
Probabilistic Country Risk Score forecasting for selected Latin American countries belonging to each cluster (cluster 1: Chile; cluster 2: Brazil, Mexico, and Uruguay; cluster 3: Ecuador; cluster 4: Paraguay, Argentina, Bolivia, and Venezuela). The dashed line is the mean of the 95% confidence interval and the red lines correspond to 95% confidence intervals between April 2, 2012, and May 5, 2013. The drawn black points are the known CRS of the 1st data set. The green points are the actual and current available data (from August 13, 2012, to November 5, 2012) used in order to validate the model, the 2nd data set. Notice that every plot has its own numerical range in the vertical axis.
In Figure
Randomness can be attributed not only to sampling errors in the data but also to the inherent complexity of the phenomenon under study. This statement particularly holds in dealing with economic problems such as forecasting CRS, since there are large jumps in CRS points in several countries with a difference of a week (until 4.2 CRS points in Trinidad and Tobago). Therefore, it is more realistic to construct predictions by confidence intervals. To calculate these intervals, let us use an adaptation of the statistical technique usually referred to as Cross-Validation or rotation estimation [
The version of the Cross-Validation process we propose is the following: we have 18 CRS data sets, one for each country, for 25 different time instants between February 6, 2012, and August 6, 2012 (1st data set, the black points in Figures we take the CRS data corresponding to we fit the model with each one of the we substitute each one of the obtained we compute the model outputs for the for each time instant, we have
Among all the values of
Considering the relevance of each cluster for our study, we have selected at least one representative country taking into account the different performance of CRS in the countries belonging to each cluster. Thus, we selected Chile as representative of cluster 1; Mexico, Brazil, and Uruguay of cluster 2; Ecuador in case of cluster 3; and Paraguay, Argentina, Bolivia, and Venezuela as representatives of cluster 4.
In Figure
According to the obtained results, we should remark the difficult task of forecasting the complex economic dynamics of 18 Latin American countries considering the current international scenario, added to the uncertainty in the global economy. The CRS data behavior reflects these circumstances, since for some CRS data, we can observe sudden large jumps in a short time.
Now, we proceed to predict and validate the model. With this aim, we gather new actual and current CRS data, in particular, from August 13, 2012, to November 5, 2012 (the 2nd data set), data that we did not use initially to fit the model because they were not available at that time. These new data allow us to compare and validate the obtained predictions from our model with the new real data.
Looking at Figure
The 95% confidence intervals provide a quite accurate forecasting for almost all considered countries, being not wider than 5 CRS points for all the countries for early May 2013. In fact, the confidence intervals contain almost all the black points (98.44%) and a high percentage of the green points (73.08%). Also, most of the points outside the 95% confidence intervals are relatively close to them. On average, the total percentage of points that lie inside the confidence intervals is up to 89.77%, a high rate taking into account the uncertainty of economic and political situation of some countries.
Argentina and Venezuela are the countries where the forecasting has been least valid. In particular, in the case of Argentina, it can be noted that from late January 2012 (42.29 CRS points) to early November 2012 (34.46 CRS points), there is a drop of 8 CRS points due to the tensions derived from the government interference and expropriation. However, the 95% confidence intervals forecast the period August 13th until September 24 when a drop of a CRS points is experienced because of its rising expropriation risk and its nonpayment debt risk. A right prediction of 6 weeks in Argentina is not a minor issue due to its instability. However, we only predict correctly 3 weeks for Venezuela.
In countries like Uruguay, Ecuador, and Bolivia, we also have a right prediction of 6 weeks, when an increasing jump of 1.2–1.9 CRS points arose the last week of September. However, we should take into account that, if the jumps had happened 1-2 months later, the corresponding 95% confidence intervals would have captured these jumps. In some way, the jumps are predicted by the model with a delay. This does not happen with Argentina or Venezuela. On the other hand, quite accurate predictions are obtained for countries with an economic and political stability such as Chile, Brazil, Mexico, and Paraguay since the 95% confidence intervals provided by the Cross-Validation contain all the points considered for validation. In fact, it is possible that, for these stable countries, our forecasting keeps valid longer than the 3 months of available data (2nd data set).
Thus, our model allows for predicting a longer period of time for most of the countries. However, the economic and political instability of some countries involves jumps of some CRS points which might not permit the predictions to be as accurate as in the more stable countries.
Worldwide financial crisis and changes in sovereign debts ratings have also affected the Latin American financial markets and their economies. However, Latin America's relative resilience to more acute rise in risk seen in other regions like Europe during last years is offering investors new options to improve risk-return trade-offs. Country Risk Score (CRS) represents a measure of the level of confidence on each country and a measure of its economic health. Latin America, a regional grouping of several countries, has also invariably succumbed to increased risk this year, according to Euromoney’s Country Risk Survey, in line with the global trend.
In this work, we present a dynamic diffusion model to study the evolution of the Country Risk Score (CRS), for a total of 18 Latin American countries, which considers both the endogenous effect of each country politics and the contagion effect among them. Using data of CRS, we fit the model with the data estimating unknown autonomous behavior and transmission parameters. Then, we use an adapted Cross-Validation technique in order to provide probabilistic predictions over the next months (August 2012 until May 2013) taking into account that most of CRS data should be inside the confidence intervals corresponding to their time instants.
The obtained results depict quite well the evolution of the CRS for most of the countries, despite the jumps and uncertainty in the CRS data within some periods. Chile is still holding its own as the darling of the region, and Brazil remains the second safest. However, whereas seems to be more confidence in Uruguay and Ecuador, faith in Venezuela and Argentina has diminished alarmingly. The increased perception of risk stems from a range of domestic and external factors, from political and economic policy failings in Argentina to worries about the impact of dissipating global growth prospects for the region’s exports.
As we have remarked, it should be pointed out that mathematical modelling with probabilistic predictions may be a powerful tool where policy makers and investors are able to design strategies, modify the model parameters in order to simulate them, and analyse the effect of changes. Looking back at the last year, the Latin American score decline is relatively mild in comparison with the falls seen in countries which belong to the Eurozone and Central and Eastern Europe. As we can check in our results even over next months horizon, Latin America might held up fairly well, despite an average score loss driven by drops for Argentina and Venezuela.
This work has been partially supported by the Spanish M.C.Y.T. Grants MTM2009-08587 as well as the Universitat Politècnica de València Grant PAID06-11 (ref. 2070).