Bank Risk Determinants in Latin America

Systemic Banking crises are a recurrent phenomenon that affects society, and there is a need for a better understanding of the risk factors to support prudential regulation and reduce unnecessary risk intake in the financial system. This paper examines the main bank risk determinants in Latin America. The period analysed covers the timespan from 1999 to 2013, including the systemic banking crisis episodes in Argentina (2001-2003) and Uruguay (2002-2005). We apply a new data-driven comparable methodology to classify and select commercial banks from the sample. We study bank risk proxied by the Z-score. In the analysis, we apply bank specific, macroeconomic and regulatory variables. We use the system-GMM estimator as our main empirical analysis method. Our results show negative relationships between the profitability and the liquidity of a bank and its risk and a positive relationship between bank asset quality and its risk. However, we find a negative correlation between good management and bank risk. We perform several robustness tests by applying alternative methodologies, and the results are similar to those our original model.


Introduction
Banking crises and banking regulation are recurrent topics in the economic policy debate.
Since 1972, banking crises have frequently affected developing and transitioning countries to a greater extent than others (Caprio, and Klingebiel, 1997).
Nonetheless, there is a lack of understanding of the factors that generate banking crises.
Regulators tend to assist more than to resolve the causes of insolvent institutions (E.J. Kane, 2016). Prudential regulation has been taken to limit excessive bank risk-taking and capital shortages in an attempt to protect society (E. J. Kane, 2016). Unfortunately, these controls tend to arrive too late after a crisis is already spreading. Therefore, a better understanding of the risk factors could be helpful in further reducing risk, mainly when regulators' powers enable authorities to address situations of distress before they spread to the wider financial system (Schich and Byoung-Hwan, 2010).
In this paper, we study the determinants of bank risk in Latin America. The market particularities of Latin America create an interesting case study. Latin American markets are highly concentrated and with significant barriers to entry that might increase the risk of financial distress (Enoch et al., 2016). In highly concentrated banking systems as in Latin America, the collapse of a banking institution can cause distress in the entire financial system. The five largest banks in South America hold three-quarters of the total banking assets of the region (BIS;. Agnoli and Vilan (2008) show that banking systems in Latin America have higher concentrations and market power than European and Asian banking systems; meanwhile, Laeven and Valencia (2012) show that banking crises are treated differently in advanced and emerging economies since macroeconomic policies tend to be applied in developed countries while the bank restructuring approach is more popular in developing countries. Another singularity of Latin American economies is inflation and hyperinflation, which influences the banking sector, financial leverage and financing sources. Latin America has a special pressure from rising interest rates due to financial risks and market volatility (Enoch et al. 2016). Additionally, risks persist in the case of possible changes in US policies, principally those towards greater trade protectionism or those that increase the risk aversion of investors, which is a consequence of the tighter financial conditions of international markets (Banco de España, 2017).
Our results show a negative relationship between good management, profitability and the liquidity of a bank and its risk. Unexpectedly, we find a positive relationship between bank asset quality and its risks. Thus, our results are of interest for Regulators and Policy makers since we determine the main variables associated with increased bank risk. We are also contributing to explaining and predicting bank crises (Sherbo and Smith, 2013).
The remainder of the paper is structured as follows. We provide a thorough review of the related literature in the following section. In section 3, we provide the data analysis; and in section 4, we show our main results and provide a further discussion.

Literature review and research hypotheses
There are differing approaches to studying the determinants and leading indicators of banking crises in the literature. Some authors (e.g., Eichengreen andRose, 1998 andKaminsky et al., 1999) focus on macroeconomic shocks while some others (e.g., Kaminsky et al. 1999) examine the macroeconomic factors (e.g., the ratio of domestic credit to nominal GDP, the real interest rate on deposits, the ratio of lending-to-deposit interest and the deficit as a percentage of the GDP, among others). In emerging markets, banking crises are associated with four main macroeconomic factors: macroeconomic volatility (e.g., large relative price changes, trade fluctuations, interest rates and changing capital flows), connected lending, government involvement and the failure of prudential regulation (Eichengreen and Rose, 1998).
The recent literature analysing banking crises complements the previous analysis by considering both bank specific and macroeconomic variables (e.g., Elsinger, Lehar, and Summer, 2006;Oet et al., 2011;and Lang and Schmidt, 2016). Specifically, Elsinger, Lehar, and Summer (2006) develop a framework where they identify that the main sources of systemic risk are the level of correlation of a bank's portfolio exposure, high bankruptcy costs and ineffective crisis resolution strategies.
In the latest years, we found large developments in early warning systems that combine macroeconomic and microeconomic variables. For instance, Oet et al. (2011) include Electronic copy available at: https://ssrn.com/abstract=3606491 bank specific and macroeconomic variables to identify imbalances that can be associated with bubbles, which explain financial stress (e.g., Securitization, the currency exchange rate concentration, bank capital at risk, the economic value of loan portfolio, Leverage, GDP, Property, investments, Leverage, the interest rate, Credit to GDP, Solvency and Credit). This holistic approach is observed in the latest publications on early warning systems by several central banks (e.g., Borio et al. 2018;Ito et al. 2013;Nyman et al. 2018). In our study, we follow this approach by selecting Regulatory, Macroeconomic and Bank specific variables.
Across the literature we find a broad spectrum of variables that explain banking risk. To conduct a systematic analysis and classification of these variables, we use the CAMEL (Capital, Asset Quality, Management, Earnings and Liquidity) 3 rating system. This rating system was introduced in the 80s by US supervisors for the on-site examinations of banking institutions and it allows supervisors to assign financial institutions a rating based on different variables. This rating enables comparisons of banks with their peers over time (Stackhouse, 2018).
The CAMEL rating is extensively used in the literature (e.g.: Chiaramonte et al., 2015;Mäkinen and Solanko, 2017). The CAMEL classification is also applied by several international banking supervisors to classify the variables in their Early-Warning Systems (e.g., Lang et al. 2018;Nyman et al.2018).
Following the CAMEL procedure, we develop 5 hypotheses.

Capital (adequacy)
Capital can be defined as the variable that determines a financial institutions' robustness to withstand shocks to its balance sheet (Gelos, 2015). Capital can also be defined as a measure of a bank's sensitivity to difficulties since bank's losses end up reducing bank capital (Lang and Forletta 2019).
Some authors describe the existence of a negative relationship between a bank's capital and its risk (affecting, by extension, the overall systemic risk level). The bank capital ratio can also be used to explain the propagation speed of distress across financial institutions (Gelos, 2015).
Usually, Capital adequacy is measured through reversed leverage proxies (e.g., Lopez, 1999;Stackhouse 2018;Bornemann et al, 2017). Therefore, we select the ratio Equity to total assets (E/TA) as a proxy for Capital Adequacy (Uhde andHeimeshoff ,2009 andChortareas et al., 2011). As equity decreases, with total assets remaining constant, the proportion of debt of a bank will rise, causing higher leverage and increasing their risktaking (Vazquez and Federico, 2012). Therefore, we state the following: Hypothesis 1: There is a negative relationship between capital adequacy measured through the E/TA of a bank and its risk.

Asset (Quality)
According to the Federal Deposit Insurance Corporation 4 , asset quality measures "the quantity of existing and potential credit risk associated with the loan portfolio, other real estate owned and other assets, as well as off-balance sheet transactions". Assets can be affected by the market valuation and other risks (i.e., reputational, compliance or strategic risk), which could affect the assets' pricing (Rono and Traore, 2018). There is a broad consensus concerning the existence of an inverse relationship between a bank's asset quality and its risk (Agrestietal, 2008).
In the literature, we can find different measures of asset quality, such as the following examples: non-performing loans to total gross loans, the sectoral distribution of loans to total loans, and the share of loan loss provisions to total average loans .
Due to the limited data on non-preforming loans, we use Net loans to total assets (NL/TA) as a proxy of asset quality. As we will develop in more detail in section 3, our sample is constituted only by commercial banks, which tend to decrease lending to improve solvency ratios (Maurin and Toivanen, 2012).
Another reason to use the NL/TA ratio as a proxy of asset quality is to analyse the lending focus of the institution in comparison to the overall business. Based on recent experiences, it is not necessarily beneficial for commercial banks to venture into nontraditional businesses (e.g., pensions, insurance, asset management and investment banking, among others) to increase their profitability. Well-known commercial banks such as NatWest, Barclays and Deutsche bank failed in these ventures and finally returned to the original retail business. We support Miller (1998) who states that the idiosyncrasies of each business should make commercial and investment banking separate.
Poor asset quality in a bank will lower interest income, increase provisions and affect the regulatory capital of a bank (Baudino et al, 2018). An increase in a bank´s regulatory capital will affect its profitability and, by extension, the bank risk. Thus, we hypothesize the following: Hypothesis 2: There is a negative relationship between the asset quality (NL/TA) of a bank and its risk.

Management (quality)
Management measures the performance of the individuals in leadership roles at a bank.
Regulators expect a bank to operate in a safe and sound manner, promoting a culture of compliance (Stackhouse, 2018).
Management is proxied in the literature by the cost to income ratio, earnings, the interest margin, the natural logarithm of total assets and efficiency, among others (Petropoulos et al., 2017, Demsetz and Strahan, 1997. We use the Cost to income ratio (CostI) to measure the management efficiency (ECB, 2012;BIS, 2017;Francis, 2014). More efficient banks tend to have lower average costs and lower cost-to-income ratios (Huljak et al, 2019), which are further expected to reduce the probability of a bank failure .
Banks with higher costs and lower levels of efficiency may be tempted to take on higher risks to compensate for the lost returns. Thus, we hypothesise the following: Hypothesis 3: There is a positive relationship between "bad quality" management measured through CostI and a bank's risk.

Earnings
Earnings and profitability indicators are used to assess the financial health and monitor the efficient allocation of a bank's resources (Agresti et al., 2008). Banks that lose money over significant periods of time do not remain in business. Like other firms, banks do not stay in business unless they are profitable (Stackhouse, 2018).
Some common metrics to proxy this category are the return on assets, the return on equity (e.g., Altunbas, et al. 2007; C-C Lee et al., 2013), interest margin to gross income, or noninterest expenses to gross income (e.g., Agresti et al., 2008;Petropoulos et al., 2017). To measure Earnings, we use the Return on Equity (ROAE). This ratio measures the profitability of a bank's assets.
An increase in bank competition could drive to a more expensive cost of capital that could encourage risk taking (Altunbas et al., 2007;C-C Lee et al., 2013). Banks need stable and increasing profits. To obtain stable profitability, a bank must manage its risk, capital and profitability to develop a business that augments capital resources over time (Greuning et al., 2009). Therefore, for our fourth hypothesis, we state the following:

Liquidity
Liquidity is related to the fundamental maturity transformation mission of a bank, which consists of transforming deposits and other liabilities into loans. Since the maturity of deposits and loans can differ, the bank needs to manage its liquidity by meeting deposits outflows at the same time it satisfies the demand for loans (Stackhouse, 2018). Petropoulos et al. (2017) and Köhler (2012) measure liquidity by comparing loans to different types of assets (e.g., loans to customer deposits, loans to total assets, and loans to volatile liabilities). Furthermore, Vazquez and Federico (2012) use the liquidity coverage ratio to show the relationship between a bank's dependence on short-term funding to finance the expansion of their balance sheet and their risk. Additionally, Greuning et al. (2009) determine that the liquidity risk of a bank is related to the bank's dependence on limited sources of funding.
Similar to Bogdan et al. (2015) and Vazquez and Federico (2012), we proxy liquidity using the Loans to Customer Deposits (L/CD) ratio. Bogdan et al. (2015) and Vazquez and Federico (2012) prove that banks with a dependence on using short-term funding to finance their balance sheet in the period prior to a crisis are more likely to fail during the crisis. A bank with an L/CD ratio above 100% is financing its loans with wholesale funding. Wholesale funding is less stable than customer deposits and could trigger an increase in the bank`s funding liquidity risk (Bonfim et al, 2012). Therefore, our last hypothesis posits the following: Hypothesis 5: There is a negative relationship between the liquidity of a bank as measured by its L/CD and its risk.

Sample
We chose banks based on the available data in the Bankscope database maintained by Bureau Van Dijk. To minimize any incoherence and possible bias related to the bank business idiosyncrasies, we include only commercial banks in our sample. Moreover, to limit the potential for selection bias, we include banks that ceased their activities and others that might have changed the name due to an acquisition or further structural changes in the sample.
The range of the period we cover is from 1999 to 2013, which includes the crises in  Table 1). Nevertheless, four countries cannot be included since there was no information about their banks (Cuba, El Salvador, Haiti and Nicaragua). Entities with abnormal ratios or extreme values are eliminated from the sample as outliers.
The criterion used to remove observations is that they are below the lower bound or above the upper bound. The formula for the lower bound is Q 5 1 -1,5* IQD 6 . The formula to calculate the upper bound is Q3 + 1,5* IQD.

Business model classification
We use the BIS Bank business model classification (Roengpitya et al., 2014) to select only retail-commercial banks. All banks from the sample need to fulfil specific conditions to be classified as "commercial". Following Roengpitya et al. 2014, we select those banks from our sample with ratios of growth of gross loans to growth of total assets, deposits to total assets and stable funding that are higher than those from trading banks (a trading bank would have ratios of approximately 25,5%, -38% and 48.6%, respectively).
Additionally, we choose banks with proportions of Interbank Lending to total assets divided by Interbank liabilities to total assets and Trading to total assets that are lower than the ratios of trading banks (a trading bank would have ratios of approximately 21.8%, 19.1%, and 17.3%, respectively).

Dependent variable
The Z-score (Z-score) is our primary measurement of the individual banks' risk. The Zscore measures the distance to default of a bank from an accounting point of view and the inputs to the calculation are the return on assets and the volatility of the return on assets.
The higher the Z-score ratio is, the greater the distance to default and, consequently, the lower the risk; conversely, the closer the Z-score is to zero, the higher the risk and the greater the probability of default. Therefore, the Z-score is indirectly proportional to bank risk.
It is common in the literature to use the Z-score to measure bank risk (Maudos, 2017;Uhde and Heimeshoff 2009;Kumar and Ravi, 2007). Recently, Sherbo and Smith (2013) 5 Quartile 6 Interquartile Distance Q1-Q3 analysed the Z-scores of the financial crisis period (from December 2007 to June 2009), proving that this risk measurement is significant with 99% confidence.
We calculate the Z-score as follows: where ROAAi,t represents the return on average assets of bank I in year t, ETAi,t denotes the ratio of equity to total assets and σ(ROAA) it is the standard deviation of the return on total average assets.
Since the Z-score is highly skewed, we use the natural logarithm of the Z-score (Laeven and Levine, 2009;Liu, Molyneux and Wilson, 2013). Schaeck and Cihk (2007) prove that the frame to calculate the Z-score in their sample does not affect their results and Yi (2012) computes the Z-score for two consecutive periods. Thus, we adjust the Z-score calculation to use a two year rolling window to increase the number of observations.

Explanatory variables
As we have previously stated in the Literature review section, we rely on the CAMEL rating system to select our explanatory variables. We present the description of the variables and expected signs in table 2.

INSERT TABLE 2 ABOUT HERE
The first CAMEL variable proxy we use for the Bank´s Capital is the E/TA. We expect a negative relationship between bank capitalization and the bank risk since a lower E/TA means higher leverage, which makes the institution less resilient to shocks (Mäkinen et Solanko, 2017). In addition, the E/TA ratio is highly correlated with the Basel III regulatory definition of the leverage Ratio (ECB, 2015).
Asset quality is examined through the NL/TA ratio since banks with higher NL/TA ratios should be able to generate more interest revenue (García-Herrero et al, 2007) and bank profitability is a key component to increase an institution's financial stability (ECB, 2015).
Electronic copy available at: https://ssrn.com/abstract=3606491 The management quality of a bank is measured by CostI. An increase in CostI is expected to reduce the probability of a bank failure Maudos et al., 2004;Demsetz and Strahan , 1997).
To measure bank's earnings, we use the ROAE. We expect a negative sign for the relation between the ROAE and distress since increased profitability reduces the likelihood of a distress event (Altunbas et al., 2007;Greuning et al., 2009).
Liquidity is proxied by the L/CD ratio (Bonfim et al 2012). The relationship between the liquidity and bank risk is expected to be negative since banks that finance their loan portfolios with short term liabilities might be exposed to refinancing problems in scenarios of macroeconomic stress since in these circumstances banks may find it difficult to raise short-term funds and customer/wholesale deposits, eventually incurring deposit drainages (Chiaramonte et al, 2015).

Control variables
The Moreover, Reinhart and Rogoff (2013) conclude that the there is a correlation between the peaks in the current account balance and new defaults on sovereign debt. Similarly, Laeven and Valencia (2008) show that most banking crises occur in countries with large current account deficits, while Kauko (2012) establishes that credit growth combined with current account deficits contributed to vulnerabilities in the banking system. Thus, we control for Current accounts (CurrAcc).
We further control for domestic credit to the private sector (DCPS) since it plays an economically and statistically significant role in predicting subsequent crises. Obstfeld We, therefore, control for Inflation % (Infl) in our multivariate analysis.
We further control for the Unemployment rate % (Unemp), which is related to bank asset quality in the previous literature (Bofondi and Ropele, 2011). A higher unemployment rate may affect the bank risk associated with lending (Hancock and Wilcox, 1994).

Finally, we have selected four indicators from the World Bank database on Bank
Regulation and Supervision developed by Barth, Caprio, and Levine (2004) to control for regulatory differences across Latin American countries in our empirical specification since the literature suggests that these indicators may affect bank risk.
The Activity restriction index (Ares) includes restrictions on securities, insurance, and real estate activities plus restrictions on banks owning and controlling nonfinancial firms.
Capital Stringency (CStr) captures whether the capital requirement reflects certain risk elements and deducts certain market value losses from capital before minimum capital adequacy is determined.
Official supervisory power (OSP) is connected to whether supervisors have the authority to take specific actions to prevent and correct problems and circumstances that can help to prevent banks from engaging in excessive risk-taking behaviour, thus improving bank development, performance and stability. Private Monitoring (PriM) levels show the degree to which banks are forced by the supervisory authorities to disclose accurate information to the public and whether there are incentives to increase market discipline.
These regulations, which promote and facilitate the private monitoring of banks, are associated with better banking-sector outcomes.
Following Uhde and Heimeshoff (2009), we control for Industry concentration using the Herfindahl-Hirschman Index (HHI). In their investigation, Uhde and Heimeshoff (2009) show that there is a negative relationship between market concentration and European banks' financial robustness.
We further control for bank size using the natural logarithm of its total assets (TA).
Across the literature, several studies indicate that larger banks are capable of improving their profitability levels and capital buffers, which decrease their assumed risk intake (Boyd et al. 2004;Kleinov et al, 2015). Additionally, these banks are capable of benefitting from economies of scale and scope (Boyd and Prescott 1986). Araújo and Leao (2013) indicate that larger institutions, as measured through the TA, in Brazil tend to be less risky.

Methodology
The first analysis we undertake is a univariate analysis using the t-test to examine the relationship between the Z-score and the different explanatory variables. To do this, we first divide our sample into two subsamples based on their distance to default. The "Low risk" subsample contains the institutions with above average Z-scores, whereas the "High risk" subsample contains the institutions with below average Z-scores. Secondly, we run two-tailed t-tests under the null hypothesis that there are no differences in the means between the high and low risk institutions.
Secondly, we apply the GMM-System estimator developed by Arellano and Bover (1995) and Blundell and Bond (1998), which is also referred to as the system-GMM estimator.
Since some bank-specific factors of bank risk can be endogenous, some other unobserved characteristics could cause correlations between the coefficients of the explanatory variables. We apply the GMM-System estimator in a two-step estimation procedure with finite-sample corrected standard errors, as proposed by Windmeijer (2005) and L. Baselga-Pascual et al. (2015).
The system-GMM estimator addresses endogeneity by means of suitable instruments. We consider the bank-specific variables as endogenous covariates by employing the lagged first differences of the bank-specific explanatory variables as instruments for the equation in levels and the lagged values of the explanatory variables in levels as instruments for the equation in differences (in line with Arellano andBover, 1995, andBlundell andBond, 1998). Industry concentration and macroeconomic variables are treated as strictly exogenous following the authors Delis andStaikouras (2011) andL. Baselga-Pascual et al (2015). Our baseline equation is as follows: (1) Z − Score = c + β. ROAE − ϕ. CostI − φ.

Univariate test
The purpose of this analysis is to study the significant differences between the "High risk" and "Low risk" subsamples according to the explanatory and control variables.  Table 4 provides the results of the system-GMM estimator. Based on the sign and significance of the coefficients, we can confirm hypotheses 1, 2 and 5. Looking at the E/TA, we observe a positive and significant relationship with the Z-score, which indicates that banks with less risk have higher E/TA ratios, as we state in hypothesis 1. This finding is in line with the literature since the correlation between equity levels and the resilience of a bank is well addressed. Secondly, we find a negative relationship between the NL/TA and bank risk since banks with higher NL/TAs present less risk. Consequently, we can confirm our second hypothesis, showing that asset quality is indicative of more lending and less risk. From this result, we can also conclude that within the commercial banks, a larger portfolio of loans is related to lower risk. This result could indicate that banks focused on the basic activity of maturity transformation tend to have less risk. Finally, the liquidity seems to be positively correlated with the bank risk (negatively correlated with the Z-score value). Thus, we can confirm our fifth hypothesis since the proxy L/CD is statistically significant and positively related to bank risk in the GMM. Banks with lower proportions of their loan portfolios being financed by customers' deposits take on more risk.
We cannot, however, confirm our third and fourth hypotheses. The coefficient of CostI is positive but not significant, and this can be explained by the different cost structures of the institutions across the Region. Additionally, the baking structure inefficiency in this area is left over from the high inflation periods. During times of high inflation, banks' revenues obtained from holding government bonds indexed to the overnight interest rate compensate for the deposit rate, which allows banks not to pay enough attention to the efficiency levels and thus the costs across the region continue being high (Singh, 2003).
Regarding the ROAE, we cannot confirm our hypothesis 4 due to the statistical insignificance of the coefficient.
Looking at the control variables, we find that the Int rate and the TA are significant. The interest rate seems to be negatively related to banks' risk intake, which can be easily understood due to the idiosyncrasy of the zone, where banks are used to working with high interest rates and larger spreads. In the same way, the TA seems to be negatively correlated with bank risk. In some of the larger economies of the zone, such as Brazil, there has been a consolidation process across the sector, which allows the institutions to achieve economies of scale and scope and become more efficient.

Robustness
To test the robustness of our results, we alternatively apply an OLS regression with fixed year and country effects (see table 5). The results of the OLS confirm hypotheses 1 and 2, showing significant and negative correlations between capital ratios, asset quality and bank risk, thus providing robustness to our results. However, based on the OLS regression, we cannot confirm hypothesis 5. There are some differences regarding the control variables since TA and Int lose their significance. However, due to the higher accuracy of the GMM-system and the confirmation of hypotheses 1 and 2, we find robust confirmation of the results.

Conclusions and discussion
In this paper, we examine the main bank risk determinants in Latin America. The conclusions of this study can be relevant for regulators in their aim to reduce the spread and limit the consequences of financial crises. The results can also be useful to identify institutions under distress.

This article empirically analyses the factors influencing commercial bank risk in Latin
America from the period from 1999 to 2013 using a panel data set of 13,365 observations.
The period of analysis includes the systemic banking crises in Argentina (2001Argentina ( -2003 and Uruguay (2002)(2003)(2004)(2005). We proxy risk using the measure of the Z-score. The bank riskdeterminants are classified following the CAMEL Rating. We complement our analysis by controlling for macroeconomic and regulatory variables. Additionally, we perform a robustness check by applying an alternative methodology that provides similar results to our original model.
One innovative factor of our analysis is the classification of the different bank business models in the sample by selecting only commercial banks, following Roengpitya et al (2014). We further examine the bank-specific, regulatory and macroeconomic determinants of bank risk and how they affect risk as measured by the Z-score (Maudos, 2017;Uhde and Heimeshoff, 2009;Kumar andRavi, 2007 andSmith 2013). We apply a quantitative approach using the system-GMM estimator, developed by Arellano and Bover (1995) and Blundell and Bond (1998), to analyse dynamic panel models.
Supporting the previous literature, we find a negative relationship between a bank's capital adequacy, asset quality, liquidity and risk. Capital adequacy indicates that banks with strong equity levels are more resilient to shocks (ECB, 2011;Gelos 2015 (Calicenan et Zhou, 2018).
Regarding the macroeconomic variables, our results show a negative relationship between interest rates and bank risk. Contrary to some European countries, LATAM countries seem to have lower risk intake when higher interest rates exist, which shows the idiosyncrasy of the region and indicates that LATAM banks work better in high interest rates scenarios where they can be more selective in their asset allocations. We further find a negative relationship between bank size and risk.
In conclusion, the risk factors that affect financial institutions in Latin America are relevant topics of analysis since these institutions continue being confronted with significant challenges, including a weak economic environment, currency devaluation and interest rate volatility, that reduce profitability and increase risk.
It will be interesting to study the macroeconomic effects on bank risk in more detail, as      The table reports the results of two-tailed t-tests comparing the means between financial institutions with higher and lower risk levels. The high and low risk subsamples consist of financial institutions with below and above median Z-Scores, respectively. The risk characteristics are classified using the CAMEL rating are defined as follows.
To measure Capital, we use the ratio of equity to total assets. The variable we use to measure the Asset Quality is the ratio of Net loans to Total Assets. For Management, we use the variable Cost to Income. Earnings are the Return on Equity as a percent. To measure Liquidity, we use the ratio Loans to Customer Deposits. ***, ** and * denote significance at the 0.01, 0.05, and 0.10 levels, respectively  Arellano and Bover (1995) and Blundell and Bond (1998).
The risk characteristics are classified using the CAMEL. Table 2 provides a description of the independent variables. The bank level variables are considered to be endogenous (ETA, NLTA, CostI, ROAE, LCD, and TA) while the macroeconomic/regulatory are considered to be exogenous (HHI, Ares, CStr, OSP, PriM, DCPS, Int, Infl, CurrAcc, GDPgrow, and Unemp). We report heteroskedasticity-consistent asymptotic standard errors in parentheses, and the significance levels are indicated as follows: ***= significant at the 1% level, **= significant at the 5% level, and *= significant at the 10% level. z1 is a Wald test of the joint significance of the reported coefficients, which are asymptotically distributed as F under the null hypothesis of no significance, with the degrees of freedom in parentheses. mi is a serial correlation test of order i using residuals and first differences, which are asymptotically distributed as N(0,1) under the null hypothesis of no serial correlation. Hansen is a test for over-identifying restrictions, which are asymptotically distributed as χ 2 under the null hypothesis of no correlation between the instruments and the error term, with the degrees of freedom in parentheses. Notes: The table reports the determinants of LATAM bank risk during the period from 1999-2013 using the alternative model specifications for our baseline equations. The risk characteristics are classified using CAMEL and Table 2 gives a description of the independent variables. R2 is the proportion of the variation in the dependent variable explained by the model. Hausman is a test that compares the fixed versus random effects, which are asymptotically distributed as χ2 under the null hypothesis that the individual effects are uncorrelated with the other regressors in the model, with the degrees of freedom in parentheses.