· web viewgiven information asymmetries common within less developed countries, we sought to...

International Finance – Fin 663Final Project Team Ashta

Natalia Drullinsky

Matt Hamilton

Keith EllisonSaunvit Pandya

Identifying Emerging Markets Bond Mispricing with Economic and Social Development Metrics

Natalia DrullinskyKeith Ellison

Matt HamiltonSaunvit Pandya

March 2nd, 2014

Abstract

Emerging Market Bonds is an asset class that has been gaining size over the past several decades, with significant growth still projected. Given information asymmetries common within less developed countries, we sought to find mispriced sovereign and corporate bonds by analyzing their relationships to certain data indicators. By analyzing Emerging Market credit spreads across a combination of Human Capital and Business Environment indicators we were able to develop a trading strategy of Emerging Market debt, with improvements over

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International Finance – Fin 663Natalia

Drullinsky Keith Ellison

Final Project Team Ashta Matt HamiltonSaunvit Pandya

traditional fixed-income trading strategies. For many investors, being exposed to Emerging Market Debt can still seem too risky, however, for other investors this strategy can provide helpful insight before making an investment decision.

I. Introduction

Every day it seems harder to find an active investment strategy that beats the market. In an effort to create an active investment strategy that outperforms the market, we decided to investigate and analyze the Emerging Market Debt (EMD). We believe that EMD is an asset class that still needs to develop and in which market imperfections can be found. Our investigation is based on the thesis that EMD spreads may not fully incorporate all the information available in the market, existing arbitrage opportunities.

To prove our thesis, we analyzed different factors that could affect EMD spreads, such as risk factors, human capital indicators, and country development indices, among others. We found an optimal statistical model to find mispriced EM Sovereign debt, which included three variables related to Business Environment and one related to Human Capital. Based on the statistical models, we developed a sovereign trading strategy using CDS instruments. We also found an optimal statistical model to find mispriced EM Corporate debt, which included two variables related to Business Environment and one related to Human Capital. However, due to lower sample size the model has limited applicability.

The research is organized as follows. In the second section, we describe the EMD market and its evolution over the last decades. In the third section, we introduce the different data sources used, explain the reasons behind the countries we chose for the analysis, and describe the challenges we encountered when constructing the data base. In the fourth section, we explain the results found when analyzing the data and the optimal statistical models to find misprices assets. In the fifth section, we explain the EM Sovereign Debt trading strategy using CDS instruments. In the sixth section, we mention future refinements and areas to explore of our analysis. In the final section, we offer some concluding remarks.

II. Emerging Market Debt: Corporate Bonds and Sovereign Debt

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Emerging Market Debt (EMD) is an interesting asset class to investigate and analyze. It offers attractive returns to investors and the possibility to increase portfolio diversification. It is also an ideal asset class for active management due to information inefficiencies in Emerging Markets and its implications in asset prices (mispriced assets).

EMD can be classified in two categories: EM Corporate Bonds and EM Sovereign Debt. The first one refers to debt issued by EM Central Governments, primarily to finance Government operations. The latter, refers to debt issued by EM Companies, with the objective of financing the firm’s operations.

Both categories of debt are structurally different, but have some similar features. When compared with a developed nation’s debt, EM debt tends to have a lower credit rating (most EMD issuance is rated below investment grade) and is traded at higher credit spreads, relative to the U.S. Treasury. This is because they are exposed to EM political risk, foreign exchange rate risk, illiquidity risk and bankruptcy risks, among other factors that are less likely to be found in more established financial markets. Since investors demand a higher return for these additional risks, EM debt offers a premium or a higher yield than debt from developed markets.

According to Erb, Harvey and Viskanta (“Understanding Emerging Market Bonds”) EM bonds have high volatility and negative skew. These are two relevant characteristics when estimating expected returns and thus, should be considered when analyzing an investment decision. Additionally, the authors added that EM debt has a low correlation with other asset classes. Incorporating assets with low correlation to a portfolio increases diversification; increasing the expected return of the portfolio, while maintaining constant the level of risk.

Both, EM Corporate debt and Sovereign debt can be issued in local currency or in foreign currency. Debt denominated in local currency is more volatile than debt issued in US dollars due to its exposure to currency risk, local interest rate risks and capital control risk. Even though local debt issued in local currency generally is exposed to higher volatility, it still may be the adequate investment decision depending on the investors’ risk profile and market expectations.

Emerging Market economies and financial markets have been developing and improving in terms of political stability, financial strength and human capital. These improvements have made EMD more appealing to investors and, as a result, this asset class has rapidly expanded. Over the last two decades, EMD has grown at a 14% annual rate reaching US 14 trillion in 2012, a 10.6% growth compared with 2011. Additionally, when comparing the size of this asset class with other credit markets, we observe that EMD is a bigger market than the US Treasury Market ($10.9 trillion in 2012), to the Global Corporate Bond Market ($8

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trillion in 2012), and to the Securitized Market (Developed Countries) ($6.7 trillion in 2012). Figure 1 shows the evolution of EMD since 1995.

Within this increase in EMD, the share of local currency bonds (corporate and sovereign debt) respect to total EMD has increased as well. In 2012, local currency bonds reached 88%, higher than 78% in 1995. We believe that one of the main explanations for this change is that the shift in many Emerging Markets from a fixed exchange rate to a floating exchange rate has lowered the currency risk (although this still is an important factor of the overall risk of the asset class), giving investors a greater degree of confidence within this asset class.

Additionally, EM Corporate debt issuance has rapidly increased, surpassing EM Sovereign debt issuance. Graph 1 shows the distribution of EM Corporate Debt and EM Sovereign Debt issuance over the last decade. We believe this shift in the relative importance of Corporate and Sovereign Debt issuance is due to two main reasons. First, improving credit fundamentals has reduced the need of governments to borrow in external markets. Second, Emerging Markets’ corporations have increased debt issuance to finance their expansion plans and continue to grow. Graph 2 shows EMD issuance by type of debt (Corporate vs. Sovereign). By the end of 2012, Emerging Market Sovereign Debt represented a ~52% of EM outstanding debt, and it is expected that in the near future Corporate outstanding will surpass EM Sovereign debt.

19951997

19992001

20032005

20072009

2011$0$2$4$6$8

$10$12$14$16

Graph 1: Emergin Market Total Corporate Debt and Sovereign Debt (USD Trillions)

Source: J.P. Morgan (Includes External and Local Currency Sovereign and Corporate Debt)

In terms of geographical distribution, during the late 90’s and the early 2000’s Latin America dominated the issuance of EMD. However, Asia, Europe and Africa have been rising their bond issuance in recent years, overpassing Latin American share of total EMD. Graph 3 shows the change in the share of EMD.

Graph 3:

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20002001

20022003

20042005

20062007

20082009

20102011

2012$0

$50$100$150$200$250$300$350

Graph 2: EM Total Cor-porate Debt and Sover-

eign Debt Issuance (USD Millions)

Corporate Debt Sovereign Debt




Source: BAML

Finally, it is important to have in mind that EMD cannot be categorized as one large homogenous market. There are several alternatives, each with different underlying risks and expected returns. As mentioned EMD includes Corporate and Sovereign debt issued in local or foreign currencies, and from countries that are very different from each other. Before making any investment decision, investors should analyze the different alternatives the EMD offers.

III. Data Selection and Processing

Data SourcesIn conducting our analysis, we focused dependent and independent variables. On the dependent side, we were analyzing credit spreads and trying to establish relationships between certain economic and social indicators as a means of predicting credit spreads and opportunities to find trading strategies based on opportunistic relative values. For credit spreads we relied on data from CEMBI, EMBI, and the World Bank (see below). For our various economic and social indicators, we sourced data from the PRS Group, the Central Intelligence Agency, The World Bank and the International Finance Corporation (see below). Although there was overlap of certain indicators we wanted to utilize difference sources as a means of triangulating our analysis. Particularly, we were interested in utilizing the CIA data because it is more difficult to scrub and get in excel for data analytics purposes. We thought this might provide an opportunity over more traditional and easy to scrub data due to the greater information asymmetry.

The PRS Group: The PRS Group has been focused on producing political risk analysis since 1979. Its rankings are recognized worldwide as being the

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58%

10%

26%

6%

Share of total EMD in 2012

Asia Easter Europe Latin America Africa

42%

10%

37%

12%

Share of total EMD in 2000

Asia Easter Europe Latin America Africa




original system for quantifying and rating political risk. In our analysis, we specifically analyzed PRS’s International Country Risk Guide, which is comprised of 22 variables in 3 subcategories of risk: political, financial, and economic. The political risk index is benchmarked with scores ranging from zero to 100, whereas the financial and economic indexes are benchmarked with scores ranging from zero to 50. While there are subsets of each ranking index, we focused on the overall category measure. For example, political risk includes categories such as government stability, corruption, ethics and tensions, etc., economic risk includes categories such as GDP per capita, real GDP growth, inflation rates, etc., and financial risk includes categories such as foreign debt as a percent of GDP, exchange rate stability, net liquidity, etc. There were 19 countries in these data sets that had a sufficient time series to run our regressions. Those countries ranged from Argentina to Egypt to Venezuela. 1

Central Intelligence Agency: The United States Central Intelligence Agency gathers data across a variety of political, social and economic categories and annually publishes the World Factbook. The origins of the Factbook go back to 1948 when he National Security Council authorized the National Intelligence Survey program. In 1955 Congress determined that the NIS was an invaluable publication and there was a continuing requirement to keep the survey up to date. The first classified Factbook was published in 1962 and the first unclassified version was published in 1971. Despite the Factbook including data from all 267 countries, often there is gaps from year to year depending on the country and data point. For the purpose of our analysis, we selected five categories to focus on from the Factbook, specifically, (1) Gini Index, (2) Unemployment Rate, (3) GDP Real Growth Rate, (4) Industrial Production Growth Rate, and (5) Inflation Rate. 2

International Finance Corporation: The International Finance Corporation is the private investment arm of the World Bank. The goal of the organization is to eliminate extreme poverty b 2030 and to boost shared prosperity in every developing country. The IFC offers investments, advisory, and asset management services to spur private sector development. The organization is owned and governed by is member countries. In addition to its direct investment work, the IFC gathers a substantial amount of data. This is aggregated and published in its annual Doing Business Rankings. The Doing Business report began in 2003 and covers over 180 countries. For the purpose of our analysis, we selected three categories to focus on from the

1 The PRS Group International Country Risk Guides, 2003-20132 The CIA World Factbooks, 2003-2013

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Doing Business rankings, specifically, (1) Contract Enforcement, (2) Legal Rights, (3) and Credit Risk Index. 3

The World Bank: The World Bank is a United Nations international financial institution that provides loans to developing countries for capital programs. The official goal of the World Bank is to reduce poverty with a commitment guided by the promotion of foreign investment and international trade to facilitate capital investment. As part of its broad reaching programs, the World Bank collects a substantial amount of data across topics that range from health to economy to infrastructure. The World Bank provides free and open access to its data. For the purpose of our analysis, we selected a broad range of categories that included both economic and social indicators. For example, some of the categories utilized in our data analysis include (1) Life Expectancy, (2) Literacy Rates, (3) Graduation Rates, (4) Cost to Start a Business, and (5) Investment Protection Index. 4

EMBI: EMBI, or the Emerging Market Bond Index, is a benchmark for measuring the total return performance of international government bonds issued by emerging market countries that are considered sovereign and meet specific liquidity and structural requirements. JP Morgan produces on of the most popular EMBI indices. For our analysis, we had monthly credit spreads for a wide variety of countries dating back to 2003.5

CEMBI: CEMBI, or the Corporate Emerging Market Bond Index, is an offshoot of EMBI and is a benchmark for measuring the US denominated corporate bonds issued by emerging market entities. JP Morgan produces on of the most popular CEMBI indices. For our analysis, we had monthly credit spreads for corporates in a wide variety of countries dating back to 2003. 6

Selected CountriesOur analysis started with over 50 emerging market countries. However, due to limitations on consistent data sets, both in terms of time series as well as reported data across benchmarks, we ultimately reduced our country count to around 15 countries. A map of our selected dataset is below:

3 International Finance Corporation Doing Business Rankings, 2003-20134 The World Bank Data Analysis5 JP Morgan EMBI Data6 JP Morgan CEMBI Data

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Data Processing and NormalizationA significant amount of time was spent preparing our data for analysis. Due to inconsistent formatting, missing data points and lack of standardization across benchmarks our team needed to clean the data. Roughly 40% of the data needed to be reformatted or cleansed in order for us to run regressions. Specifically, we had to remove bad data and missing data points. Additionally, we had to make certain adjustments for items such as European formats (commas vs. periods) and establishing common units (percentages, basis points, etc.). Additionally, some data points were annual where as others were monthly or quarterly. We made adjustments using the arithmetic mean (for discrete data such as rankings) or geometric mean (for cumulative data such as returns) when appropriate to standardize such data sets. All the data was then complied into a master document with various lookup functions to add to the pliability as a means of facilitating out statistical analysis.

Additionally, we a significant portion of the data required normalization. Many of the data sets had different measuring metrics. For example, dollar amounts for cost of starting a business, day counts for bankruptcy, or various indexes for other categories. In order to run our regression with minimal noise, we normalized the data sets to all have a zero to one benchmark. When applicable, we used an absolute normalization (normalization relative to all other countries, even those excluded in the analysis). This was primarily done for the World Bank variables with finite limits. Additionally, when the absolute normalization was no possible we used a relative normalization between the specific sample sets. This was primarily done for Doing Business rankings and quantitative metrics such as contract enforcement.

Challenges Encountered

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In conducting our analysis, there were several challenges that we had to overcome to generate meaningful data points to test our hypothesis. The challenges that we encountered were primarily with respect to the data we were able to obtain and either the lack thereof of the lack of consistency between data sets. While we were able to analyze the data and generate output and trading strategies, to implement an actual strategy based on our analysis would require a deeper dive into specific data sets. Below are the four main difficulties:

Dynamic Credit Spreads vs. Static Indicators: Our credit spreads from CEMBI and EMBI were fluid whereas many of the economic and/or social indicators had little to no change, even in the long term. For example, Brazil’s political risk ranking has does not really change from year to year despite its constantly changing credit spread. This situation makes it harder to develop forecasts based on indicators that have a high correlation to the relative value of comparable credit spreads.

Monthly vs. Annual Data: Our credit spreads from CEMBI and EMBI were in monthly outputs; however, the economic and social indicators were reported annually. The more static data points from the economic and social indicators made it difficult to capture event driven changes in the credit spread that might be tied to an economic or social event in any given month but that would not be shown in the data. Additionally, to compare the data sets we annualized the monthly data using geometric means. Doing so enabled us to run regressions yet limits the ability to extrapolate periods of high volatility.

Limited Historic Data Points: Much of the data we sourced from CEMBI, the World Bank, the IFC, and the CIA was limited in its duration. The vast majority of data points were limited to less than a decade. Additionally, across countries data was not consistent from a time series perspective across countries. This limited our scope of potential countries that we were capable of analyzing. For example, across different data sources, we were able to get underlying data for over 50 countries. However, of those 50 countries, there were only approximately 15 that went back far enough with consistent data points to actually run regressions. The limited time series led to less variable inputs into the regressions, which makes is difficult to extrapolate sound findings.

Illiquid Markets for Trading Strategies: The trading market for corporate high yield emerging market credit is limited to nonexistent, depending on the country. This limits the data available to service our underlying analysis. Additionally, for the data that is available, it is often illiquid and thus the price quotes may not reflect the actual price that one would expect to pay in

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the market. Due to these constraints, it was difficult to test specific trade ideas, such as shorting Chilean corporate high yield bonds because in a real setting such a trade would be negotiated between two counterparties.

IV. Analysis Methodology and Results

MethodologyOur analysis was set up in a paired format whereby an independent variable dataset was merged with a dependent variable dataset. This led to 6 discrete analysis pairs: CEMBI v. CIA, CEMBI v. World Bank, CEMBI v. ICRG, EMBI v. CIA, EMBI v. World Bank, and EMBI v. ICRG. The actual integration process consisted of the following steps:

1. Selection of Columns from Master File – Columns for the dependent variable and independent variables were copied and pasted from the master file into a blank Excel workbook.

2. Filtering of Bad and Missing Data – All rows in the new file were manually scrubbed for NA’s, other Excel errors (usually due to bad geometric means), and missing data.

3. Standardization of Years and Countries – Since we wanted regression models to translate fairly and easily into potential trading strategies, we kept the years for all countries in a regression to be the same. This meant that occasionally we had to throw out some years (2003, 2004, 2012, and 2013) which would have increased the number of data points and added statistical significance, but we felt that the resulting tradeoff in terms of simplicity (all countries in a model have 8 years of data, for example) would prevent bias and was worth the marginal precision.

Once the integration was finished, we were ready to run multivariate regressions on one combined dataset pair. The process that we followed consisted of mechanical steps demanded by the regression program (RegressIT, also known formerly as FSBForecast) as well as our own method for investigation, iteration, and refinement based on our knowledge of statistics:

1. Creation of Variable Named Ranges – Mechanical Step – In order for RegressIT to be able to use the data in a regression, it has to be organized into columns and named as a named range. Luckily, the program automates this in order to make the process painless.

2. Regression Setup – Mechanical Setup – Once named ranges were defined, the next step was to set up a test regression. RegressIT requires every regression model to be setup using an interactive menu-driven system.

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Through this system, one can choose independent variables, the dependent variable (if there are multiple in one dataset), regression options (such as setting intercept to zero), variable transformations (lead, lag, log-log, etc), in addition to advanced setting such as time series options.

3. Overall Model – Our approach – In our approach, we began by running the regression on all the independent variables. The reason for doing this was twofold: 1) we wanted to get a sense for the overall fit of the dataset and 2) we wanted an assessment on future areas of refinement.

4. Model Refinement – Our approach – We then iterated on additional models by focusing on independent variables that seemed to yield the most explanatory power. The statistical parameters that we used to determine explanatory power included: R2, adj. R2, standard error of regression, the absolute value of the t-statistic (and the associated p-value). Note that on most models, we set the intercept to zero purposely in order to not have a statistically significant “other” parameter (which would have been tough to mimic in a trading strategy).

Note that an optional 5th step exists – Forecasting. The functionality for setting aside data (backtesting) and using it for forecasting is provided by the RegressIT program. However, due to our limited data set, we elected to develop and verify “perfect hindsight” trading strategies instead of taking part of the data and using it for forecasting (at the cost of having less precise models).

Results: EMBI v. ICRG

All Variable Model

As described above, the first regression model that we ran in each paired case was to test all the independent variables against the dependent variable. For EMBI-ICRG, the “all variable” model was simple to interpret since the ICRG dataset that we used only had two variables: political risk and economic/finance risk.

The regression statistics seemed promising (Figure 1) although the histogram of residuals was non-normal and exhibited significant skew (Figure 2). We then looked at the theoretical versus actual residuals (because ideally that’s what the trading strategy was going to try to capture – Figure 3), and we saw an interesting phenomenon – mispricing occurs both at the negative and positive ends despite an overall positive skew.

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Regression Statistics: Model 1 for EMBI_ICRG_Spreads (2 variables, n=198)R-Squared Adj.RSqr Std.Err.Reg. # Cases # Missing t(2.50%,196) Conf. level

0.527 0.523 280.068 198 0 1.972 95.0%There is no universally accepted definition of R-squared and adjusted R-squared for a model w ith no intercept.The mean value of the residuals is not necessarily zero w hen there is no intercept. In this model it is 15.617.

Summary Table: Model 1 for EMBI_ICRG_Spreads (2 variables, n=198)Variable Coefficient Std.Err. t-Stat. P -value Lower95% Upper95%ICRG_Eco___Finance_Risk 6.742 1.964 3.433 0.001 2.869 10.614ICRG_Political_Risk -3.354 2.266 -1.480 0.140 -7.823 1.115

Figure 1: Regression Statistics for All Variable Model (no time) for EMBI-ICRG.

Figure 2: Histogram of Residuals for All Variable Model (no time) for EMBI-ICRG.

Figure 3: Theoretical v. Actual Residuals for All Variable Model (no time) for EMBI-ICRG.

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Modified All Variable Models with Time Added

Due to the scarcity of independent variables, we decided to test whether a time variable could add explanatory power. There are two methods for doing this – using the raw time data (in years) or using a time index benchmarked to the first year in the dataset. As shown below in Figures 4 and 5, we tried both approaches. Statistically, the time index is a more unbiased (and hence correct) approach whereas using raw years can potentially add distortion due to the fact that the base value could distort the regression coefficients (especially if intercepts are not used).

We were able to verify this empirically – Figure 4 with the Time Index shows similar regression statistics to Figure 1 above. The better regression model in Figure 5 may be due to actual information embedded in the time variable; however, a portion of the better fit is also likely due to the statistical distortion mentioned above.



Summary Table: Model 3 for EMBI_ICRG_Spreads (3 variables, n=198)Variable Coefficient Std.Err. t-Stat. P -value Lower95% Upper95%ICRG_Eco___Finance_Risk 5.983 2.122 2.819 0.005 1.797 10.169ICRG_Political_Risk -3.028 2.293 -1.321 0.188 -7.551 1.494time_index 6.043 6.407 0.943 0.347 -6.592 18.679

Figure 4: Regression Statistics for All Variable Model (time index) for EMBI-ICRG.



Summary Table: Model 4 for EMBI_ICRG_Spreads (3 variables, n=198)Variable Coefficient Std.Err. t-Stat. P -value Lower95% Upper95%ICRG_Eco___Finance_Risk -15.276 2.033 -7.513 0.000 -19.287 -11.266ICRG_Political_Risk -17.409 1.844 -9.443 0.000 -21.045 -13.773time 1.293 0.089 14.581 0.000 1.118 1.468

Figure 5: Regression Statistics for All Variable Model (time in years) for EMBI-ICRG.

Assuming that the time data did add some explanatory power, we went ahead and generated a histogram of residuals and a theoretical v actual plot in order to

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benchmark a potential trading strategy (Figures 6 and 7). We used this model (with time in years) as the model for our trading strategy.

Figure 6: Histogram of Residuals for All Variable Model (time in years) for EMBI-ICRG.

Figure 7: Theoretical v. Actual Residuals for All Variable Model (time in years) for EMBI-ICRG.

Results: CEMBI v. ICRG

We did a similar analysis with CEMBI spreads.

All Variable Model

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The CEMBI model without time had a much stronger fit with the ICRG data (Figure 8) than the corresponding EMBI spreads. Intuitively, this could signify that corporate debt is much more responsive to the financial indicators of the economy than the underlying sovereign (possibly due to central bank activities, but that’s another topic).

The R2’s are significantly higher while the political risk becomes less of an explanatory variable than before, which is in line with our intuitions on sovereigns.

The big caveat here with the CEMBI data is that the better fit could be primarily due to the lower sample size as we have 66 observations for CEMBI as opposed to 3 times as many for EMBI. Evidence to the effect can be seen in the fact that the t-statistic for the economic/finance risk is lower (the p-value is higher) while the goodness of fit improves. Statistically, this means that the model is a better fit even though the underlying models explain less, which could mean that sample size is at play.

Regression Statistics: Model 1 for CEMBI_Spreads (2 variables, n=66)R-Squared Adj.RSqr Std.Err.Reg. # Cases # Missing t(2.50%,64) Conf. level


Summary Table: Model 1 for CEMBI_Spreads (2 variables, n=66)Variable Coefficient Std.Err. t-Stat. P -value Lower95% Upper95%ICRG_Eco___Finance_Risk 4.280 2.522 1.697 0.094 -0.757 9.318ICRG_Political_Risk -0.262 3.011 -0.087 0.931 -6.278 5.753

Figure 8: Regression Statistics for All Variable Model (no time) for CEMBI-ICRG.

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Figure 9: Histogram of Residuals for All Variable Model (no time) for EMBI-ICRG.

Figure 10: Theoretical v. Actual Residuals for All Variable Model (no time) for EMBI-ICRG.

The histogram of residuals (Figure 9) also shows the impact of sample size with a higher proportion of extreme observations and a lower degree of normalness. However, Figure 10 attests to the goodness-of-fit.

Modified All Variable Models with Time Added

When we adjust the CEMBI model by adding time as a variable, we see improvements through using both indices (normalized to the first year in the dataset) as well as overall time in years. This leads us to believe that the time

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dimension provides us more tangible information (and less statistical distortion) than the EMBI (sovereign) equivalent.

Same as with above, the model with time in years marginally outperforms the model with time as an index. We went ahead and chose the model with time in years as the model of choice for the development of our CEMBI-ICRG trading strategy.

Figures 11 and 12 below show regression parameters for both models containing a temporal dimension. Figures 13 and 14 show the histogram of residuals (with skew) and a theoretical v. actual plot for the model with time in years.



Summary Table: Model 3 for CEMBI_Spreads (3 variables, n=66)Variable Coefficient Std.Err. t-Stat. P -value Lower95% Upper95%ICRG_Eco___Finance_Risk 1.651 2.649 0.623 0.535 -3.643 6.944ICRG_Political_Risk 1.436 2.977 0.482 0.631 -4.514 7.386time_index 16.131 6.518 2.475 0.016 3.107 29.155

Figure 11: Regression Statistics for All Variable Model (time index) for CEMBI-ICRG.


0.866 0.860 136.364 66 0 1.998 95.0%There is no universally accepted definition of R-squared and adjusted R-squared for a model w ith no intercept.The mean value of the residuals is not necessarily zero w hen there is no intercept. In this model it is -0.052.

Summary Table: Model 2 for CEMBI_Spreads (3 variables, n=66)Variable Coefficient Std.Err. t-Stat. P -value Lower95% Upper95%ICRG_Eco___Finance_Risk -5.099 2.651 -1.924 0.059 -10.395 0.198ICRG_Political_Risk -10.758 3.089 -3.483 0.001 -16.931 -4.586time 0.725 0.128 5.662 0.000 0.469 0.981

Figure 12: Regression Statistics for All Variable Model (time in years) for EMBI-ICRG.

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Figure 13: Histogram of Residuals for All Variable Model (time in years) for CEMBI-ICRG.

Figure 14: Theoretical v. Actual Residuals for All Variable Model (time in years) for CEMBI-ICRG.

Results: EMBI v. World Bank

The World Bank multivariate regression models were extremely challenging due to the number of permutations possible even with a limited dataset and a limited number of independent variables. In order to keep this write-up to a manageable level, we do not include figures and reasoning for all the permutations and models we attempted.

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Rather, we begin with an all variable model, whose performance we summarize. We then follow this up with a summary table of regression statistics for all the various models that we attempted (along with reasoning). Finally, we end with a detailed exploration of the optimal model that we selected as the basis for our trading strategy. Additionally, given the goodness of fit and explanatory power embedded in the base variables (as well as the limited project time and data sample size), we did not investigate the statistical impact of adding a time dimension to the World Bank data.

All Variable Model

The All Variable Model featured EMBI spreads being regressed on 11 World Bank business environment and human capital metrics. The total number of observations was 84, which was lower than the 198 used for ICRG data. The difference was due to the number of common entries (an “inner merge”) between the World Bank dataset and the EMBI spreads data.

Overall, the regression statistics were very impressive (Figure 15), though much of the goodness-of-fit was due to the limited sample size. In the aggregate data set, 8 out of the 11 metrics had an absolute t-statistic greater than 2.5 with 5 having a t-statistic greater than 3. This gave us confidence that the World Bank dataset did have significant explanatory power that models could extract and that trading strategies could leverage.



Summary Table: Model 6 for EMBI_ICRG_Spreads (11 variables, n=84)Variable Coefficient Std.Err. t-Stat. P -value Lower95% Upper95%Child_Motality 335.554 371.513 0.903 0.369 -404.870 1,076WB_Cost_Start_Business -7,462 2,753 -2.710 0.008 -12,949 -1,975WB_Cost_to_Enforce_Contract -1,011 380.398 -2.657 0.010 -1,769 -252.588WB_Credit_Info_Index 100.627 88.738 1.134 0.261 -76.228 277.482WB_GDP_per_Capita -4,827 1,458 -3.311 0.001 -7,733 -1,921WB_Invest_Protect_Index -388.657 250.665 -1.551 0.125 -888.232 110.918WB_Labor_Force -566.306 107.255 -5.280 0.000 -780.066 -352.546WB_Life_Exp_Female -1,321 467.536 -2.826 0.006 -2,253 -389.687WB_Life_Exp_Male 1,524 504.622 3.020 0.003 518.012 2,529WB_Proced_Start_Biz 517.720 127.223 4.069 0.000 264.164 771.275WB_Years_Mandatory_Edu 695.935 200.868 3.465 0.001 295.607 1,096

Figure 15: Regression Statistics for All Variable Model for EMBI-World Bank.

Many of the variables were significantly positively or inversely correlated as shown by the correlation matrix in Figure 16.

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Correlation MatrixVariable EMBI_ICRG_Spreads

EMBI_ICRG_Spreads 1.000 Child_MotalityChild_Motality -0.134 1.000 WB_Cost_Start_Business

WB_Cost_Start_Business 0.078 0.013 1.000 WB_Cost_to_Enforce_ContractWB_Cost_to_Enforce_Contract -0.133 0.177 0.222 1.000 WB_Credit_Info_Index

WB_Credit_Info_Index 0.202 0.194 0.169 0.039 1.000 WB_GDP_per_CapitaWB_GDP_per_Capita 0.108 -0.302 -0.284 -0.026 0.084 1.000 WB_Invest_Protect_Index

WB_Invest_Protect_Index -0.237 0.609 0.098 0.455 0.490 -0.097 1.000 WB_Labor_ForceWB_Labor_Force -0.192 -0.024 -0.151 -0.467 -0.214 -0.333 -0.212 1.000 WB_Life_Exp_Female

WB_Life_Exp_Female 0.170 -0.957 0.087 -0.049 -0.127 0.262 -0.550 0.003 1.000 WB_Life_Exp_MaleWB_Life_Exp_Male 0.203 -0.762 0.258 0.007 0.030 0.192 -0.381 0.162 0.847 1.000 WB_Proced_Start_Biz

WB_Proced_Start_Biz 0.275 -0.209 -0.052 -0.347 -0.167 -0.046 -0.403 0.442 0.278 0.201 1.000 WB_Years_Mandatory_EduWB_Years_Mandatory_Edu 0.300 -0.306 0.257 0.242 0.195 0.557 -0.048 -0.218 0.359 0.455 -0.026 1.000

Figure 16: Correlation of Independent Variables within the World Bank Dataset and with EMBI spreads.

The resulting goodness of fit and relative “normalness” of the distribution can be seen in the histogram of residuals and theoretical v. actual plots in Figures 17 and 18.

Figure 17: Histogram of Residuals for All Variable Model for EMBI-World Bank

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Figure 18: Theoretical v. Actual Residuals for All Variable Model for EMBI-World Bank.

Summary of Models After seeing the regression statistics from an all variable model, we next attempted regressions based solely on business metrics or human capital indicators. We isolated variables that seemed to have the most explanatory power and then we recombined the two to see if the overall explanatory power seemed to have increased. Figure 19 shows the regression statistics for the different models while Figure 20 shows a table describing each model.Dependent Variable: EMBI_ICRG_Spreads

Model Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7Run Time 2/13/14 10:06 PM 2/13/14 10:07 PM 2/13/14 10:08 PM 2/13/14 10:09 PM 2/13/14 10:10 PM 2/13/14 10:11 PM 2/13/14 10:13 PM

Regression StatisticsR-squared 0.685 0.594 0.640 0.626 0.642 0.791 0.701

Adjusted R-squared 0.660 0.589 0.617 0.621 0.633 0.759 0.686Standard Error of Regression 179.693 197.600 190.725 189.734 186.820 151.326 172.847

# Cases 84 84 84 84 84 84 84Regression Coefficients: Beta (p-value)

InterceptChild_Motality -178.853 (0.502) 335.554 (0.369)

timeWB_Cost_Start_Business 1661.204 (0.519) -7462.042 (0.008)

WB_Cost_to_Enforce_Contract 319.191 (0.369) -1010.719 (0.010)WB_Credit_Info_Index 315.998 (0.001) 283.486 (0.000) 126.808 (0.020) 100.627 (0.261) 265.437 (0.003)

WB_GDP_per_Capita 1029.531 (0.310) -4826.845 (0.001)WB_Invest_Protect_Index -546.45 (0.005) -388.657 (0.125) -549.481 (0.000)

WB_Labor_Force -138.09 (0.115) -566.306 (0.000)WB_Life_Exp_Female -408.651 (0.384) -1321.486 (0.006)

WB_Life_Exp_Male 524.533 (0.328) 1523.722 (0.003)WB_Proced_Start_Biz 340.931 (0.000) 265.782 (0.001) 517.72 (0.000) 238.325 (0.014)

WB_Years_Mandatory_Edu 284.851 (0.138) 361.91 (0.000) 695.935 (0.001) 321.857 (0.008)

Figure 19: Regression Statistics for All EMBI-World Bank models.

Model # Description and Variables Used6 All Variable Model1 Business Metrics Only2 Business Metric – Credit Info Only

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3 Human Capital Metrics Only4 Human Capital Metric – Years Mandatory Education Only5 Combined Model – Procedures to Start a Business, Credit Info Rating7 Combined Model – Credit Info, Procedures to Start a Business,

Protection of Investment Rating, and Years Mandatory EducationFigure 20: Model “Glossary” for EMBI-World Bank finalized models. Note: Preliminary permutations were on the order of 20-30 and are not featured or included in the scope of this write-up.

Optimal Choice ModelWe decided on an optimal model by weighing the tradeoffs between simplicity and accuracy. While it is true that the overall all-variable model has the highest R2

parameters, much of that may be due to sample size distortion (11 independent variables means 10 degrees of freedom!). With the number of observations, our goal was to have a model with 3-5 variables, with each coefficient being fairly significant, and the overall regression statistics being fairly positive.

With these criteria in mind, we chose Model #7 as the optimal model for trading strategy formulation for EMBI-World Bank.

Regression statistics, the histogram of residuals, and theoretical v. actual residual plots can be seen in Figures 21 through 23 below.


0.701 0.686 172.847 84 0 1.990 95.0%There is no universally accepted def inition of R-squared and adjusted R-squared for a model w ith no intercept.The mean value of the residuals is not necessarily zero w hen there is no intercept. In this model it is -0.306.

Summary Table: Model 7 for EMBI_ICRG_Spreads (4 variables, n=84)Variable Coefficient Std.Err. t-Stat. P -value Lower95% Upper95%WB_Credit_Info_Index 265.437 86.168 3.080 0.003 93.958 436.917WB_Invest_Protect_Index -549.481 145.124 -3.786 0.000 -838.287 -260.675WB_Proced_Start_Biz 238.325 95.103 2.506 0.014 49.063 427.587WB_Years_Mandatory_Edu 321.857 119.217 2.700 0.008 84.608 559.106

Figure 21: Regression Statistics for Optimal EMBI-World Bank Model.

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Figure 22: Histogram of Residuals for Optimal EMBI-World Bank Model.

Figure 23: Theoretical v. Actual Residuals for Optimal EMBI-World Bank Model.

Results: CEMBI v. World Bank

We adopted a similar approach for CEMBI-World Bank as we did with EMBI-World Bank. Additionally, CEMBI-World Bank had similar problems relative to EMBI-World Bank as did CEMBI-ICRG in comparison to EMBI-ICRG, namely the limited sample size generating a high degree of statistical distortion.

This can easily be seen in the regression statistics for all the variable model below whereby the model has a very high degree of fit (as measured by R2’s and the

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standard error of regressions). However, the p-values show that none of the variables are very significant! This is because we have given the regression model 10 degrees of freedom (just in terms of independent variables) in a dataset with only 56 observations.

Luckily, we were able to trim some of the distortion by selecting variables carefully and limiting the number of independent variables in the optimal model.

All Variable Model

The regression statistics, the correlation matrix for variables, histogram of residuals, and theoretical v. actual plot of residuals can be seen in Figures 24 through 27 below. The lack of significance of variables in terms of the statistical distortion can be further evidenced by the non-normalness of the residual distribution (regardless of the Anderson-Darling statistic which proxies for symmetry). We see significant amounts of skew and kurtosis, with the negative tail being almost as high as the mean of the distribution!


0.841 0.802 165.185 56 0 2.014 95.0%There is no universally accepted def inition of R-squared and adjusted R-squared for a model w ith no intercept.The mean value of the residuals is not necessarily zero w hen there is no intercept. In this model it is 0.97.

Summary Table: Model 4 for CEMBI_Spreads (11 variables, n=56)Variable Coefficient Std.Err. t-Stat. P -value Lower95% Upper95%Child_Motality -2,581 2,665 -0.969 0.338 -7,948 2,786WB_Cost_Start_Business 1,699 2,734 0.622 0.537 -3,807 7,205WB_Cost_to_Enforce_Contract 348.855 392.336 0.889 0.379 -441.351 1,139WB_Credit_Info_Index 98.637 146.174 0.675 0.503 -195.772 393.046WB_GDP_per_Capita -639.094 981.521 -0.651 0.518 -2,616 1,338WB_Invest_Protect_Index -151.774 317.123 -0.479 0.635 -790.493 486.945WB_Labor_Force 39.105 121.726 0.321 0.750 -206.064 284.274WB_Life_Exp_Female -232.114 653.904 -0.355 0.724 -1,549 1,085WB_Life_Exp_Male 324.340 635.395 0.510 0.612 -955.412 1,604WB_Proced_Start_Biz 244.876 247.991 0.987 0.329 -254.604 744.356WB_Years_Mandatory_Edu 513.662 284.377 1.806 0.078 -59.102 1,086

Figure 24: Regression Statistics for All Variable Model for CEMBI-World Bank.

Correlation MatrixVariable CEMBI_Spreads

CEMBI_Spreads 1.000 Child_MotalityChild_Motality 0.248 1.000 WB_Cost_Start_Business

WB_Cost_Start_Business 0.234 0.746 1.000 WB_Cost_to_Enforce_ContractWB_Cost_to_Enforce_Contract 0.142 0.767 0.835 1.000 WB_Credit_Info_Index

WB_Credit_Info_Index 0.290 -0.109 -0.065 -0.174 1.000 WB_GDP_per_CapitaWB_GDP_per_Capita -0.343 -0.810 -0.416 -0.321 -0.045 1.000 WB_Invest_Protect_Index

WB_Invest_Protect_Index -0.263 -0.572 -0.158 -0.020 0.017 0.802 1.000 WB_Labor_ForceWB_Labor_Force 0.106 0.193 -0.041 -0.163 -0.209 -0.353 -0.392 1.000 WB_Life_Exp_Female

WB_Life_Exp_Female -0.326 -0.863 -0.529 -0.501 0.260 0.861 0.779 -0.255 1.000 WB_Life_Exp_MaleWB_Life_Exp_Male -0.060 -0.533 -0.186 -0.191 0.284 0.632 0.654 0.025 0.796 1.000 WB_Proced_Start_Biz

WB_Proced_Start_Biz 0.209 0.615 0.211 0.086 0.023 -0.752 -0.693 0.545 -0.667 -0.485 1.000 WB_Years_Mandatory_EduWB_Years_Mandatory_Edu 0.418 0.107 0.074 -0.004 0.526 -0.317 -0.243 -0.115 -0.126 0.027 -0.026 1.000

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Figure 25: Correlation of Independent Variables within the World Bank Dataset and with CEMBI spreads.

Figure 26: Histogram of Residuals for All Variable Model for CEMBI-World Bank.

Figure 27: Theoretical v. Actual Residuals for All Variable Model for CEMBI-World Bank.

Summary of Models We used a slightly different “optimization” methodology than we did for EMBI-World Bank. Given the limited number of observations and the amount of

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distortion, we purposely limited ourselves to 2-4 metric permutations. We tried a few dozen of these with directional refinements based on which variables had explanatory power and which didn’t. The optimal models for 2 metric, 3 metric, and 4 metric are listed below. In each of them, we endeavored to make the tradeoff between accuracy (low p-values, high R2s), information capture (improvement in fit), and simplicity (limiting variables/metrics).

Dependent Variable: CEMBI_SpreadsModel Model 4 Model 5 Model 6 Model 7 Model 8

Run Time 2/14/14 11:01 PM 2/14/14 11:02 PM 2/14/14 11:02 PM 2/14/14 11:03 PM 2/14/14 11:03 PMRegression Statistics

R-squared 0.841 0.824 0.821 0.821 0.813Adjusted R-squared 0.802 0.817 0.811 0.807 0.806

Standard Error of Regression 165.185 158.906 161.660 163.207 163.553# Cases 56 56 56 56 56

Regression Coefficients: Beta (p-value)Intercept

Child_Motality -2581.236 (0.338) 255.163 (0.842)time

WB_Cost_Start_Business 1699.1 (0.537)WB_Cost_to_Enforce_Contract 348.855 (0.379) 39.498 (0.843)

WB_Credit_Info_Index 98.637 (0.503) 106.214 (0.372) 70.051 (0.551)WB_GDP_per_Capita -639.094 (0.518) -480.72 (0.064)

WB_Invest_Protect_Index -151.774 (0.635) -148.311 (0.138)WB_Labor_Force 39.105 (0.750)

WB_Life_Exp_Female -232.114 (0.724)WB_Life_Exp_Male 324.34 (0.612)

WB_Proced_Start_Biz 244.876 (0.329) 89.622 (0.617)WB_Years_Mandatory_Edu 513.662 (0.078) 606.355 (0.000) 570.967 (0.001) 425.113 (0.000) 463.787 (0.002)

Figure 28: Regression Statistics for All CEMBI-World Bank models.

Model # Description and Variables Used4 All Variable Model5 2 Metrics – GDP and Years Education6 3 Metrics – Credit Info Rating, Investment Protection Rating, and

Years Education7 4 Metrics – Child Mortality, Procedures to Start a Business, Contract

Enforcement, and Years Education8 2 Metrics – Simple Model – Credit Info Rating and Years EducationFigure 29: Model “Glossary” for CEMBI-World Bank finalized models. Note: Preliminary permutations were on the order of 20-30 and are not featured or included in the scope of this write-up.

Optimal Choice ModelDespite Model 5 having slightly better regression statistics, we chose Model 6 as our optimal model for basing the trading strategy off of. The reason for this is that we felt that the investment protection rating and credit information more intuitively supported bond pricing than GDP per capital. Therefore, we reasoned that the statistical link between GDP and spreads in this case was more likely to be tenuous and coincidental compared to the intuitive links between investor protection, information availability (credit info rating), and bond spreads.

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Given the minute difference in regression parameters, it made sense to go with the intuitive option.

Regression statistics, the histogram of residuals, and theoretical v. actual residual plots can be seen in Figures 30 through 32 below.


0.821 0.811 161.660 56 0 2.006 95.0%There is no universally accepted def inition of R-squared and adjusted R-squared for a model w ith no intercept.The mean value of the residuals is not necessarily zero w hen there is no intercept. In this model it is 1.725.

Summary Table: Model 6 for CEMBI_Spreads (3 variables, n=56)Variable Coefficient Std.Err. t-Stat. P -value Lower95% Upper95%WB_Credit_Info_Index 106.214 117.991 0.900 0.372 -130.447 342.875WB_Invest_Protect_Index -148.311 98.397 -1.507 0.138 -345.671 49.048WB_Years_Mandatory_Edu 570.967 158.932 3.593 0.001 252.190 889.745

Figure 30: Regression Statistics for Optimal CEMBI-World Bank Model.

Figure 31: Histogram of Residuals for Optimal CEMBI-World Bank Model.

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Figure 32: Theoretical v. Actual Residuals for Optimal CEMBI-World Bank Model.

Results: EMBI v. CIA

The multivariate regression models using CIA data were a simplified extension of the modeling approach used for the World Bank. The number of independent variables were limited to 5 due to some of the data collection challenges outlined in the section above. However, we had almost as many observations as the EMBI-ICRG due to a relatively strong inner merge, in terms of countries and years, between the two data sets.

All Variable Model

We began with an all-variable model as before. The all-variable model appeared to have strong regression statistics as well as multiple variables with significant explanatory power, such as inflation. As expected by intuition, inflation correlated significantly with spreads over treasury, while industrial production correlated strongly with GDP. Despite a high degree of correlation, industrial production failed to explain spreads, as did unemployment. Regression statistics are shown in Figure 33 below, with correlations displayed in Figure 34.

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Summary Table: Model 2 for EMBI_ICRG_Spreads (5 variables, n=197)Variable Coefficient Std.Err. t-Stat. P -value Lower95% Upper95%GDP_Grow th_Normalized -128.052 124.387 -1.029 0.305 -373.393 117.290Gini_Normalized 221.705 59.912 3.701 0.000 103.535 339.874Industrial_Production_Normalized 22.402 127.698 0.175 0.861 -229.469 274.274Inflation_Normalized 914.714 77.851 11.750 0.000 761.161 1,068Unemployment_Normalized -151.725 129.836 -1.169 0.244 -407.813 104.363

Figure 33: Regression Statistics for All Variable Model for EMBI-CIA.

Correlation MatrixVariable EMBI_ICRG_Spreads

EMBI_ICRG_Spreads 1.000 GDP_Grow th_NormalizedGDP_Grow th_Normalized -0.149 1.000 Gini_Normalized

Gini_Normalized -0.108 -0.012 1.000 Industrial_Production_NormalizedIndustrial_Production_Normalized -0.184 0.810 -0.073 1.000 Inflation_Normalized

Inflation_Normalized 0.610 -0.073 -0.157 -0.156 1.000 Unemployment_NormalizedUnemployment_Normalized -0.032 -0.167 0.248 -0.088 0.047 1.000

Figure 34: Correlation of Independent Variables within the CIA Dataset and with EMBI spreads.

The histogram of residuals (Figure 35) shows a fair degree of “non-normalness” with a mild skew and kurtosis. The actual v. theoretical plot of residuals (Figure 36) shows that, despite the correlations, mispricing opportunities exist both on the low and high ends.

Figure 35: Histogram of Residuals for All Variable Model for EMBI-CIA.

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Figure 36: Theoretical v. Actual Residuals for All Variable Model for EMBI-CIA.

Summary of Models For modeling with CIA data, we used somewhat of a “brute force” approach given the limited number of independent variables. We realized early that inflation was definitely going to be an important predictive variable, leading to its appearance in all three of our final candidate models (a number of permutations were tried and culled, most of which used industrial production and unemployment).

What was slightly surprising was the significance of the GINI (a measure of income inequality in a society). Given that we focused on business metrics when extracting the CIA data, we had selected GINI with a mind towards using it as a proxy for a human capital metric. GINI, as an indicator proved to be surprising useful, and it was included in all three models as well. Note that we created a testing model with the intercept to test our initial data pull. That entry should not be confused with an operational model.

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Dependent Variable: EMBI_ICRG_SpreadsModel Model 1 Model 2 Model 3 Model 4

Run Time 2/19/14 2:07 PM 2/19/14 2:07 PM 2/19/14 2:08 PM 2/19/14 2:08 PMRegression Statistics

R-squared 0.390 0.723 0.720 0.719Adjusted R-squared 0.374 0.716 0.715 0.716

Standard Error of Regression 215.457 217.134 217.427 217.365# Cases 197 197 197 197

Regression Coefficients: Beta (p-value)Intercept 182.41 (0.047)

GDP_Grow th_Normalized -147.074 (0.236) -128.052 (0.305)GDP_Real_Grow th_Rate

Gini_FinalGini_Normalized 11.748 (0.923) 221.705 (0.000) 171.043 (0.001) 134.19 (0.000)

Industrial_Production_Growth_RateIndustrial_Production_Normalized -0.916 (0.994) 22.402 (0.861)

Inflation_Normalized 854.993 (0.000) 914.714 (0.000) 908.243 (0.000) 899.359 (0.000)Inflation_Rate

timeUnemployment_Normalized -177.397 (0.172) -151.725 (0.244) -120.061 (0.347)

Unemployment_Rate

Figure 37: Regression Statistics for All EMBI-CIA models.

Model # Description and Variables Used1 All Variables + Intercept (Used only as a test case to verify data

integrity)2 All Variables3 GINI, Inflation, and Unemployment4 GINI and InflationFigure 38: Model “Glossary” for EMBI-CIA models.

Optimal Choice ModelWe chose our optimal model on the tradeoff between accuracy (p-values, high R2s), information capture (improvement in fit), and simplicity (limiting variables/metrics). Similar to the CEMBI-World Bank case above, the model that we chose (Model 4) was not the model with the best regression statistics. However, it performed very close to the best, and it managed to do that just with two variables as opposed to three or four, thereby demonstrating the significance and explanatory power of those variables. In addition, the fact that the model was simple would make the trading strategy easier to design and execute, and in the real world, easier to transact.

Regression statistics, histogram of residuals, and a plot comparing theoretical and actual residuals can be seen in Figures 39 through 41 below.

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Summary Table: Model 4 for EMBI_ICRG_Spreads (2 variables, n=197)Variable Coefficient Std.Err. t-Stat. P -value Lower95% Upper95%Gini_Normalized 134.190 31.566 4.251 0.000 71.936 196.444Inflation_Normalized 899.359 76.088 11.820 0.000 749.297 1,049

Figure 39: Regression Statistics for Optimal EMBI-CIA Model.

Figure 40: Histogram of Residuals for Optimal EMBI-CIA Model.

Figure 41: Theoretical v. Actual Residuals for Optimal EMBI-CIA Model.

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Results: CEMBI v. CIA

We repeated the modeling exercise with CIA data for CEMBI spreads. The inner merge yielded 95 observations, less than EMBI but still a decent sample size for 5 explanatory variables.

All Variable Model

Unlike with CEMBI and World Bank, the overall, all variable model with CIA data demonstrated excellent fit and significant variables as can be seen from the Figures 42-44 below (regression statistics, histogram of residuals, and actual v. theoretical residuals). The level of distortion was limited, most likely due to smaller choice of independent variables leading to less degrees of freedom. GINI and Inflation were the most significant, with Industrial Production and GDP being moderately significant. Unemployment did not seem to correlate nor be significant.



Summary Table: Model 2 for CEMBI_Spreads (5 variables, n=95)Variable Coefficient Std.Err. t-Stat. P -value Lower95% Upper95%GDP_Grow th_Normalized 212.695 115.217 1.846 0.068 -16.203 441.594Gini_Normalized 274.270 55.413 4.950 0.000 164.183 384.357Industrial_Production_Normalized -205.029 118.301 -1.733 0.087 -440.054 29.997Inflation_Normalized 593.319 161.906 3.665 0.000 271.665 914.974Unemployment_Normalized -29.941 214.516 -0.140 0.889 -456.116 396.233

Figure 42: Regression Statistics for All Variable Model for CEMBI-CIA.

Correlation MatrixVariable CEMBI_Spreads

CEMBI_Spreads 1.000 GDP_Grow th_NormalizedGDP_Grow th_Normalized 0.013 1.000 Gini_Normalized

Gini_Normalized 0.056 -0.176 1.000 Industrial_Production_NormalizedIndustrial_Production_Normalized -0.121 0.783 -0.126 1.000 Inflation_Normalized

Inflation_Normalized 0.358 -0.024 -0.128 -0.096 1.000 Unemployment_NormalizedUnemployment_Normalized 0.102 -0.145 0.074 -0.061 0.392 1.000

Figure 43: Correlation of Independent Variables within the CIA Dataset and with CEMBI spreads.

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Figure 44: Histogram of Residuals for All Variable Model for CEMBI-CIA.

Figure 45: Theoretical v. Actual Residuals for All Variable Model for CEMBI-CIA.

Summary of Models Using what we had learned from EMBI data, we decided to throw out many of the permutations earlier on and focus on inflation and GINI, leading to 2 models plus a test model with an intercept. Note that we did try one variable/metric models, but we decided against this route to the due to the potential error (in terms of volatility and data quality) with just one metric.

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Dependent Variable: CEMBI_SpreadsModel Model 1 Model 2 Model 3

Run Time 2/19/14 2:01 PM 2/19/14 2:02 PM 2/19/14 2:03 PMRegression Statistics

R-squared 0.169 0.834 0.827Adjusted R-squared 0.123 0.825 0.823

Standard Error of Regression 143.794 143.633 144.242# Cases 95 95 95

Regression Coefficients: Beta (p-value)Intercept 100.907 (0.374)

GDP_Grow th_Normalized 183.785 (0.129) 212.695 (0.068)GDP_Real_Growth_Rate

Gini_FinalGini_Normalized 159.996 (0.254) 274.27 (0.000) 294.681 (0.000)

Industrial_Production_Growth_RateIndustrial_Production_Normalized -199.06 (0.097) -205.029 (0.087)

Inflation_Normalized 560.382 (0.001) 593.319 (0.000) 627.264 (0.000)Inflation_Rate

timeUnemployment_Normalized -60.711 (0.781) -29.941 (0.889)

Unemployment_Rate

Figure 46: Regression Statistics for All CEMBI-CIA models.

Model # Description and Variables Used1 All Variables + Intercept (Used only as a test case to verify data

integrity)2 All Variables3 GINI and InflationFigure 47: Model “Glossary” for CEMBI-CIA models. Optimal Choice ModelGiven our focused investigation effort, the choice of an optimal model was little surprise. We found that both GINI and inflation were very significant, and led to a strong fit. As a result, we chose Model 3 as our optimal model for a trading strategy. Additionally, as shown by the plot on actual v. theoretical residuals, we believe that a trading strategy can exploit mispricing based on this model.



Summary Table: Model 3 for CEMBI_Spreads (2 variables, n=95)Variable Coefficient Std.Err. t-Stat. P-value Lower95% Upper95%Gini_Normalized 294.681 34.717 8.488 0.000 225.740 363.623Inflation_Normalized 627.264 141.556 4.431 0.000 346.161 908.367

Figure 48: Regression Statistics for Optimal CEMBI-CIA Model.

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Figure 49: Histogram of Residuals for Optimal CEMBI-CIA Model.

Figure 50: Theoretical v. Actual Residuals for Optimal CEMBI-CIA Model.

V. Trading Strategies

Since the EM Corporate Bonds universe is vast and often illiquid, there are very few easily accessible indices or pricing proxies to test our trading theories. As such, we opted to run sample trading strategies on EM Sovereign Bonds – which were also in consideration and part of our research process. EM Sovereign Bonds still have limited indices available, especially with the countries we chose, however there was more readily available pricing data to back-test our regression results.

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Our models are trying to explain spreads fluctuations – for example, time “t” variables explaining time “t” spreads. We looked at the actual spreads versus our modeled spreads and we assumed that deviations are assumed to be a mispricing. This is also known as a "rich/cheap” model.

Given the FX fluctuations across the many countries we were considering as well as the lack of easily accessible indices, we chose to move forward with USD 10Y CDS in our trial strategies. We felt this was one trading instrument that had widespread generic pricing data available and was also dominated in USD for simplicity purposes.

It is important to note the research we preformed was meant to be a starting point for further research in this space and to ultimately develop and refine a viable trading strategy. Still, we hoped to at least do some “sniff test” to see what, if anything could have a real world trading application through time and more importantly if it would be profitable.

Calculating Model SpreadsThrough our data analysis, we identified key inputs that were more or less statistically relevant to actual spreads. This output gave coefficients to apply to normalized data inputs that produced a theoretically model spread.

In the case of EMBI and CIA data regressions, we found that GINI and Inflation were the most relevant indicators to be accounted for in terms of sovereign bond spreads. The modeled spreads can then be concurrently calculated and compared in accordingly.

The following is the output for Argentina from 2006 to 2013. Full results are available in the appendix.

Depending on if the modeled spreads are rich or cheap relative to the actual spreads, we implemented a systematic series of rules to manage trading:

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• If EMBI Spreads > Modeled Spreads = Buying Opportunity (Sell CDS)• If EMBI Spreads < Modeled Spreads = Selling Opportunity (Buy CDS)

Recall that when spreads shrink (tighten) bond prices increase and vice versa and further note that CDS and corresponding bonds inversely correlated, although not perfectly. We essentially aimed to take advantage of the market concept when investors sell CDS if they think spreads will tighten and vice versa.

Trading Execution and ResultsWith the systematic trigger in placed, we then applied to all countries in our model. With only yearly data available, the time of execution was at year-end only. Essentially, we would model a sale or purchase of CDS depending on the aforementioned rich/cheap model. The CDS exposure would be executed at market CDS prices held for the term of 1 year until the exposure was closed and the process was repeated again following the systematic process.

The results produced a profit over the 2004-2013 time period, however it is important to note this is ignoring transaction costs and actual defaults.

Further, an interesting insight from our trade strategy is the trend to reward award exposure to extremely high risk sovereigns. As such while, the model results are compelling, we would not view this as the sole trading strategy in a

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portfolio. This method might be valuable in managing tail risk of various sovereigns and could be an additional review point in the credit research process.

VI. RefinementsAdditional work could be done in expanding regressions to monthly or daily returns. This would give a higher confidence interval and further robustness of our model. We would also like to access premium indexing and pricing services (i.e. Barclays Point), a look at the prices, yields, and defaults of actual EM Corporate and Sovereign bonds would give valuable insights and provide further data points to evaluate and back-test.

We would also like to explore value in local currency debt while managing the FX and default risks, this could potentially be an area of addition return relative to the corresponding USD denominated debt. Finally, we feel it would be valuable to identify further risk metrics commonly used and/or misused by market participants and identify possible key metrics that are not factored into our analysis. Possibly the fit of our modeled could be further improved and more accurately explain the discrepancies in sovereign spreads.

VII. Conclusions

Despite the rapid evolution and relevance of Emerging Market Debt in the world’s portfolio, we can still find mispriced assets in EM Sovereign and Corporate bonds. For both types of debt, we explored the relationship between spreads and different Human Capital and Business Environment indicators and found interesting investment opportunities.

For Emerging Market Sovereign Bonds, we found an optimal statistical model that included three Business Environment indicators –credit information, investment protection, and procedures to start a business-, and one Human Capital indicator –years of mandatory education. Based on this model, we developed a sovereign trading strategy using CDS instruments and assuming 20/20 hindsight. This strategy aims to sell or purchase CDS depending on the aforementioned rich/cheap model.

On the other hand, for Emerging Market Corporate Bonds we found a different optimal statistical model, which included two Business Environment indicators –credit information, and investment protection-, and one Human Capital indicator –years of mandatory education. Unlike the Sovereign debt model, the Corporate debt model has limited applicability due to a lower sample size.

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Bibliography:

Bloomberg

“Emerging Markets Corporate Bonds: Seizing Opportunities in the 21st

Century”, , Western Asset, March 2013.

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“Emerging Market Debt: Is US Dollar-Denominated or Local Currency the

Better Bet?”, MFS® White Paper Series, June 2013.

JP Morgan EMBI Data

International Finance Corporation Doing Business Rankings, 2003-2013

The CIA World Factbooks, 2003-2013

“The Emerging Market Fixed Income Universe”, Jan Dehn, Ashmore Group,

August 2013.

The World Bank Data Analysis

“Understanding Emerging Market Bonds”, Erb, Harvey and Viskanta, October

1999.

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