This paper aims to investigate the determinants of nonperforming loans in the Romanian banking sector by means of time series modelling. It is motivated by the hypothesis that macroeconomic-cyclical indicators, monetary aggregates, interest rates, financial markets, and bank-specific variables influence the nonperforming loans in the Romanian banking system. Using monthly series that span from December 2001 to November 2010, we cover both the booming period and the recent financial crisis. Given the significant presence of the Greek banks in Romania, the novelty of the paper lies in the introduction of variables that proxy the Greek crisis. Thus, we examine the existence of a potential transmission channel to the Romanian banking system by investigating the impact of the Greek crisis to the Romanian nonperforming loans. Our findings indicate that macroeconomic variables, specifically the construction and investment expenditure, the inflation and the unemployment rate, and the country's external debt to GDP and M2 jointly with Greek crisis-specific variables influence the credit risk of the Romanian banking system. The results have several implications for policymakers, regulators, and managers as the most recent published stress tests on the Romanian banking system are based on end 2008 data.
The study of financial sector stability has become the cornerstone of modern macroeconomic policy. The recent global financial crisis highlighted the importance of appreciating financial institutions’ vulnerabilities in the context of managing credit risk. The key motivation for this paper is to improve our understanding of the credit risk determinants by focusing on the Romanian banking system while casting a vigilant eye on potential contagion effects from neighbouring countries. This is particularly important given that the Romanian financial system is dominated by foreign-owned commercial banks. Among them, the Greek banks’ subsidiaries have a substantial presence as they hold 30.7% of aggregate foreign capital while they account for the second largest market share in the Romanian banking system [
Using time series modelling techniques, this paper empirically investigates the determinants of ex post credit risk as reflected on the loss loan provisions to total loans ratio for the Romanian banking system. Related to a growing body of literature, the purpose of the study is to offer an insight into the factors that determine the quality of the loan portfolio of Romanian banks. In this direction, it utilizes a broad dataset that spans from December 2001 to November 2010. The explanatory power of macroeconomic, Romanian bank-specific, monetary, interest rates and financial markets’ variables is investigated. The key contribution of the paper lies in the introduction of proxies for the Greek debt crisis and the subsequent Greek banks’ financial distress. The aim is to examine a potential transmission channel or spillover effects to the Romanian banking system. As the twin Greek crises unfold, any repercussion for the neighbours is possibly the most important issue in the minds of policy makers, regulatory bodies, and bankers. Furthermore, the time series utilised include both the booming period as well as the downturn following the financial crisis and the ensuing manifestation of Greece’s structural weaknesses.
The paper is organized as follows. Section
The Romanian economy has evolved from a long history of defaults on sovereign debt, periods of high inflation, and banking crises. During the Great Depression period, many local and foreign banks in Romania collapsed or experienced heavy runs [
Barisitz [
By the end of the 1990s the Romanian government carried out legal reforms through the new central bank law. Retrospectively, the year 1999 proved to be a sort of structural turning point for the Romanian economy as the authorities initiated the first privatizations of major Romanian banks. Given the size of the country, the Romanian financial sector offered an impressive growth potential for foreign strategic investors. Figures
Structure of credit institutions' share capital by country of origin (H1: 2010).
Market share of credit institutions by structure of shareholding (H1: 2010).
The following years up to the burst of the global financial crisis were characterised by rapid credit growth. That was thought to be part of the ongoing process of financial deepening given that the credit to GDP ratio still remains at relatively low levels. On the other hand concerns were raised whether the economy was experiencing a credit boom, a situation where credit expands at an unsustainable pace.
It has been argued that the presence of foreign banks in Romania has increased the efficiency of financial intermediation and the availability of credit to the real economy. Yet, there are indications that financial stress originating in Euro area-based parent banks may have been transmitted to Romania. The lending survey of the National Bank of Romania (NBR) indicates that the risk profile of almost all industries rose with the riskiest sectors being construction and real estate, thus, reflecting the adverse impact of the global financial crisis. The outlook for the Romanian banking system remains negative, driven mainly by the tough economic conditions in the country following a severe recession in 2009 [
This section reviews the empirical work on the relation between macroeconomic variables and the loan portfolio quality or credit risk (the framework for studying the impact of macroeconomic variables or the business cycle on credit risk is represented by two competitive theories. The first one stresses that credit risk is procyclical whereas the second one defends the countercyclical view). Many studies investigate the factors that induce financial crises by examining potential links between bank-specific variables and macroeconomic factors.
Delving into the specifics of the crises literature, Gavin and Hausmann [
An increasingly popular method of assessing financial sector vulnerabilities is the macro stress-testing approach (the term refers to a range of techniques used to assess the vulnerability of a financial system to “exceptional but plausible” macroeconomic shocks). Relevant studies examine the link between banks’ loan losses, or Nonperforming loans, and macroeconomic factors. The most common approach in similar studies involves estimating on historical data the sensitivity of banks’ balance sheets to adverse changes in macro fundamentals. Then the estimated coefficients can be used to simulate the impact on the financial system of possible stress scenarios in the future. The focus is on credit risk, which by large represents the most significant risk faced by banking systems worldwide.
Two main strands of the literature can be identified in this area, building on the seminal works by Wilson [
Since the seminal work of Sims [
Several recent papers [
Following Virolainen [
A leading role in the development of aggregate stress tests has been performed by the IMF, in cooperation with the World Bank. In 2005 the IMF conducted for the first time in Greece a financial sector assessment program [
The above-mentioned studies, in general, corroborate theoretical postulates with respect to the macroeconomic influences on loan portfolio quality and, consequently, on banking sector stability. In effect, good economic conditions seem to be commensurate with good loan quality measured by either the nonperforming loans’ ratio or loan loss provisions.
Following the empirical literature reviewed, the primary hypothesis under test is that credit risk (credit risk is the risk that a counter-party or obligor will default on their contractual agreement. Financial institutions typically cover expected losses through appropriate provisioning) is sensitive to macroeconomic, cyclical, financial markets and bank-related factors. Therefore, the variables’ selection for the empirical part of the study is based on the indicators presented in related studies to ensure comparability of results.
This paper follows the provisioning approach. The dependent variable, credit risk, is defined as the ratio of loss and doubtful loans to total loans (LLP ratio). Several studies have used Nonperforming loans, loan loss provisions, or composite indices as the metrics to assess the vulnerability of the financial system over time. Alternative indicators considered in this study to measure the credit quality of the Romanian banking system were the credit risk ratio as defined by the NBR and the ratio of total loan defaulters to total debtors. The empirical results obtained when we used the alternative metrics were poor and inconsistent (that may be due to structural breaks in the series that could not be attributed to the financial crisis. They may arise as a result of changes in monetary regulation or reporting standards). All credit risk indicators considered are plotted in Figure
Credit risk in the Romanian banking system.
The increasing level of provisions (LLP ratio) reflects the declining asset quality of the Romanian banking system. IMF [
The complete set of the exogenous variables considered can be viewed in Table
The macroeconomic-cyclical indicators used are the GDP (the seasonally adjusted gross domestic product in nominal terms has been used. The interpolation method used to transform the macroeconomic data frequency is the quadratic (match average) method. Results should be considered as suggestive since observing a series at a lower frequency provides fundamentally less information than observing the same series at a higher frequency), the unemployment rate, the consumer price index, the foreign direct investment, the investment expenditure (gross fixed capital formation), the construction (anecdotal information suggests that the property market in Romania has weakened considerably after the global financial crisis. A sharp contraction in capital inflows may lead to a major slump and unemployment in the construction sector, with adverse consequences for Nonperforming loans) expenditure, the total consumption, and the household consumption and the foreign trade captured by the country’s imports and exports. The current account is also expected to exert a significant role as for many developing economies workers’ inward remittances save the current account from becoming an unmanageable deficit. A sharp contraction of inflows could threaten the financial stability through a drying up of credit to the private sector, resulting in a slump of economic activity and an increase of loan defaults. The “bad habit” of debt growth is also incorporated in the dataset as its dynamics may deteriorate the real economy and subsequently the credit risk.
Monetary aggregates considered are the M1, M2, and M3 with the first two being particularly important as they may serve as proxies to the GDP, thus, avoiding potential problems arising from the low-frequency series of the latter.
Any deterioration in market confidence is expected to lead to downward pressure on the exchange rate, upward pressure on interest rates, and declining equity values with the overall result being a deterioration of the Romanian credit portfolio quality. In this respect, the time series employed as financial markets’ indicators are the BET index and the BET-C index of the Bucharest Stock Exchange and the over-the-counter market index (RASDAQ). A sharp decline in the stock prices may signal adverse market perceptions of the health of the financial market. The Dow Jones Euro Stoxx 50 equity index is considered as an indicator of the Eurozone economic prospects and markets’ expectations. Through the price of oil, a commodity is introduced in the dataset that its price volatility influences almost all economies in the SEE region. A sharp weakening of the Romanian national currency (Ron) against euro would have significant adverse consequences for household and business balance sheets, leading to a substantial increase in Nonperforming loans (IMF [
The determinants of credit risk should not be sought exclusively in macroeconomic factors. A broad set of Romanian aggregated bank-specific indicators is employed to investigate the hypothesis that potential credit risk problems may reside within the core of the banking system. Credit plays a vital role in private sector led growth. Thus, the growth rate of credit is expected to influence the LLP ratio. The loans to deposits ratio serves as a liquidity proxy and gives indications of the ability of the banking system to mobilize deposits to meet credit demand. In addition, it highlights the banks’ risk appetite given the lending boom in the precrisis period. The Romanian banking system leverage ratio is considered to validate the moral hazard hypothesis, that is, low capitalisation of banks may amplify the nonperforming loans.
As already mentioned, a substantial part of the Romanian banking system assets is owned by Greek banks. As their “parents” suffer funding shocks due to the Greek sovereign debt turmoil, the local subsidiaries, albeit typically highly liquid and well capitalized [ The rapid expansion of Greek banks in Romania has resulted in imbalances and funding deficits. The unfolding of the Greek debt crisis is marked in the spread differential between the Greek and German government bonds yields. The deteriorating asset quality of the Greek banking system together with the strained liquidity is evident in the funding overreliance by the European Central Bank. The Greek banks offer(ed) investors a good emerging market exposure platform. Consequently, their SEE prospects are reflected in their shares performance in the Athens Stock Exchange.
de Haas and van Lelyveld [
The estimation process requires the selection of a set of variables that, according to economic theory and empirical evidence, affect the Romanian credit risk. Broadly, the empirical section consists of three steps. Unit root tests to ensure time series stationarity. Univariate regressions to identify the indicators with explanatory power on the LLP ratio of the Romanian banking system. Estimation, specification, and diagnostic checking of a multivariate model on the basis of variables and theory consistency.
Initially the time series were tested for unit roots by the Augmented Dickey Fuller tests. When a series is found to be integrated of order one (I(1)) or two (I(2)), the first or the second difference of the variable was taken to obtain stationarity. The data transformation(s) can be viewed in Table
Following an analogous approach to Kalirai and Scheicher [
The LLP ratio is regressed against each indicator at time
Results of the univariate regressions.
Regression on current factor | Regression on lagged factor | |||||||||
Expected sign | Coefficient | Coefficient | lag(s) | |||||||
Macroeconomic and cyclical indicators | ||||||||||
Construction | − | −0.005 | 3.15 | 0.002 | 0.09 | −0.004 | 2.58 | 0.011 | 0.06 | 12 |
Gross fixed capital formation | − | −0.005 | 3.13 | 0.002 | 0.09 | −0.003 | 2.33 | 0.004 | 0.04 | 12 |
Total gross external debt/GDP | − | −0.002 | 4.59 | 0.000 | 0.17 | 0.02 | 3.90 | 0.000 | 0.13 | 3 |
Unemployment | + | −0.001 | 1.98 | 0.049 | 0.04 | −0.001 | 3.75 | 0.000 | 0.13 | 12 |
Total consumption | − | −0.005 | 2.66 | 0.009 | 0.06 | −0.004 | 2.26 | 0.026 | 0.05 | 1 |
CPI | − | −0.001 | 5.67 | 0.000 | 0.23 | −0.001 | 4.86 | 0.000 | 0.19 | 3 |
Trade balance | − | −0.001 | 2.64 | 0.01 | 0.06 | −0.001 | 3.06 | 0.003 | 0.09 | 12 |
Monetary indicators | ||||||||||
M1 | − | −0.006 | 2.99 | 0.003 | 0.08 | −0.005 | 2.68 | 0.008 | 0.07 | 3 |
M2 | − | −0.013 | 2.96 | 0.004 | 0.08 | −0.01 | 2.50 | 0.014 | 0.06 | 3 |
Interest rates | ||||||||||
Euribor 3 month | + | −0.003 | 3.51 | 0.000 | 0.10 | −0.003 | 3.46 | 0.000 | 0.11 | 3 |
Romanian banking system indicators | ||||||||||
Credit | +/− | −0.03 | 5.16 | 0.000 | 0.2 | −0.02 | 4.10 | 0.000 | 0.14 | 3 |
Greek fiscal crisis and banking indicators | ||||||||||
Loss Loan Provisions/total loans | + | 0.46 | 2.66 | 0.009 | 0.06 | 0.40 | 2.10 | 0.040 | 0.04 | 3 |
Spread Greek-German bond | + | 0.01 | 3.24 | 0.000 | 0.09 | 0.01 | 3.00 | 0.000 | 0.08 | 3 |
Greek 10-year bond | + | 0.01 | 2.45 | 0.021 | 0.05 | 0.01 | 3.50 | 0.000 | 0.10 | 1 |
ECB Reliance Index I | +/− | 0.04 | 1.71 | 0.090 | 0.03 | 0.07 | 3.87 | 0.000 | 0.13 | 3 |
Only the significant indicators are presented on the table due to space limitations. The indicator spread Greek-German bond is also significant at lag 1.
In accordance to the main body of literature reviewed, an extensive array of the macroeconomic-cyclical indicators is found to be significant on the loan portfolio quality of the Romanian banking system. The results indicate that in each category (Table
In terms of monetary indicators, both aggregates M1 and M2 help explaining the variability in the LLP ratio with the expected direction. Interestingly, four indicators that proxy the Greek crises’ dynamics appear significant while they affect the Romanian LLP at the expected direction. This result comes hand in hand with our expectations in view of the significant Greek presence in the Romanian banking system. It provides grounds to believe that some spillover effects of the Greek debt crisis and banking system distress may have started to manifest themselves.
Building upon the significant factors identified in the bivariate regressions, we estimate a multivariate credit risk model following a general-to-specific methodology. The criteria to arrive at a parsimonious final model formulation are as follows. The indicators in the final specification of the multivariate model should bear the correct sign. Ideally, at least one significant indicator from each category should be included in the model. The model should be logically plausible and simple. Such models are easier interpreted and communicated. All variables included in the multifactor model should be significant both separately and jointly, at least at the 10% level of significance. The standard battery of residuals’ tests should indicate no pathological issues.
Following the described methodology while satisfying the criteria set previously, the time series model estimated by OLS is presented in Table
Regression output of the multivariate model.
Dependent Variable: loss loan provisions/total loans (R) | ||||
Method: Least Squares | ||||
Variable | Coefficient | Std. error | Prob. | |
0.000523 | 0.000399 | 1.311663 | 0.1932 | |
CPI | −0.000150 | 3.00E-05 | −4.997403 | 0.0000 |
Unemployment | 0.000263 | 7.70E-05 | 3.422545 | 0.0010 |
Construction | −0.003324 | 0.001184 | −2.806879 | 0.0062 |
Gross fixed capital formation (−12)* | −0.003555 | 0.001441 | −2.466644 | 0.0157 |
Total gross external debt/GDP (−3) | 0.012220 | 0.002648 | 4.614924 | 0.0000 |
M2 | −0.008042 | 0.002274 | −3.536230 | 0.0007 |
Loss loan provisions/total loans (GR) | 0.452571 | 0.194031 | 2.332466 | 0.0221 |
Spread Greek-German bond (−1) | 0.001251 | 0.000440 | 2.843167 | 0.0056 |
0.657178 | Mean dependent var | 0.000852 | ||
Adjusted | 0.624529 | S.D. dependent var | 0.001507 | |
S.E. of regression | 0.000923 | Akaike info criterion | −11.04576 | |
Sum squared resid | 7.16E-05 | Schwarz criterion | −10.80067 | |
Log likelihood | 522.6277 | 20.12818 | ||
Durbin-Watson stat | 2.261533 | Prob ( | 0.000000 |
*Numbers in parentheses indicate the lag of the respective variable used in model estimation. All indicators (individually and jointly) included in the model are significant at the 1% confidence level.
The complete dataset.
Indicators | Definition interpretation | Source |
---|---|---|
Credit risk for the Romanian banking system | ||
Loss loan provisions/total loans (R) | Doubtful and loss loans provisions to total loans granted by credit institutions, excluding off-balance-sheet items | National Bank of Romania and authors’ calculations |
Credit risk | Gross exposure related to nonbank loans and interest under “doubtful” and “loss” to total loans and interest classified related to nonbank loans, excluding off-balance-sheet items | National Bank of Romania |
Defaulters/Debtors | Number of total loan debtors to total loan defaulters (legal and natural entities) | National Bank of Romania |
Macroeconomic and cyclical indicators | ||
CPI | Consumer price index annual rate (%) | European Central Bank |
GDP | Real gross domestic product | NISR & Authors’ calculations |
Current account | Current account | National Bank of Romania |
Current account/GDP | Current account as % of GDP | Authors’ calculations |
Construction | Construction sector expenditure contribution in GDP at constant terms | NISR |
Gross fixed capital formation (investment) | Investment expenditure contribution in GDP at constant terms | NISR |
Total consumption | Total consumption contribution in GDP at constant terms | NISR |
Household consumption | Household consumption contribution in GDP at constant terms | NISR |
FDI monthly stock | Foreign direct investment, net, monthly | National Bank of Romania |
FDI annual flow | Foreign direct investment, net, accumulated | National Bank of Romania |
Trade balance | Imports minus exports (constant terms) | National Bank of Romania |
Unemployment | Registered unemployment rate (%) | National Bank of Romania and European Central Bank |
Total gross external debt | Total gross external debt (general government, monetary authority, banks, and other sectors) | National Bank of Romania |
Total gross external government debt | Total gross external debt general government (short and long term) | National Bank of Romania |
Total gross external debt banks | Total gross external debt banks (short and long term) | National Bank of Romania |
Total gross external debt/GDP | Total gross external debt as % of gross domestic product | Authors’ calculations |
Total gross external debt banks/GDP | Total gross external debt banks as % of gross domestic product | Authors calculations |
Monetary indicators | ||
M1 | Narrow money. Comprises currency in circulation plus overnight deposits. Also proxies gross domestic product | National Bank of Romania |
M2 | Intermediate money. Comprises M1 plus highly liquid deposits. | National Bank of Romania |
M3 | Broad money. Comprises M2 plus marketable instruments issued by monetary and financial institutions | National Bank of Romania |
Interest rates indicators | ||
Euribor 1 month | Historical close, average of observations (% p.a.) | Reuters |
Euribor 3 month | Historical close, average of observations (% p.a.) | Reuters |
Robor 3 month | Historical close, average 3-month interest rate on operations to place funds (% p.a.) | National Bank of Romania |
Robor 12 months | Historical close, average 12-month interest rate on operations to place funds (% p.a.) | National Bank of Romania |
Spread differential Euribor-Robor | Euribor 3M minus Robor 3M | Authors’ calculations |
Romanian long term-bond | Average interest rate of long-term Romanian government bond (% p.a.) | National Bank of Romania |
German 10-year bond | Secondary market yield of 10-year German government bond | European Central Bank |
Spread Romanian-German bond | Spread differential between Romanian and German long-term government bond yield | Authors’ calculations |
Financial markets indicators | ||
BET | The Bucharest Exchange Trading index. Comprises the ten most liquid companies listed on the BSE regulated market | National Bank of Romania |
BET-C | The Composite Index of Bucharest Stock Exchange. Represents the overall performance of all companies listed on the BSE regulated market | National Bank of Romania |
RASDAQ | The index captures the over-the-counter market | National Bank of Romania |
Dow Jones Euro Stoxx 50 | Equity index. Indicator of economic prospects and market sentiment | Datastream |
Athens Stock Exchange (general) | General Index Athens Stock Exchange ( | Bank of Greece |
Athens Stock Exchange (banks) | Banking Index Athens Stock Exchange ( | Bank of Greece |
Oil | Brent crude oil price fob in Euro per barrel | European Central Bank and authors’ calculations |
Ron/Eur | Monthly average spot exchange rate on Forex market | European Central Bank |
Ron/Usd | Monthly average spot exchange rate on Forex market | European Central Bank |
Usd/Eur | Monthly average spot exchange rate on Forex market | European Central Bank |
Romanian banking system indicators | ||
Credit | Total loans granted by Romanian credit institutions | National Bank of Romania |
Leverage ratio | Capital/assets. Key prudential indicator | National Bank of Romania |
Liquidity ratio | Effective liquidity/required liquidity. Measures banking system liquidity. Key prudential indicator | National Bank of Romania |
Loans to assets | Loans granted to clients (gross value)/total assets (gross value). Key prudential indicator | National Bank of Romania |
Loans-to-deposits ratio | Loans/deposits. Proxy for liquidity | National Bank of Romania and authors’ calculations |
Interest rate on loans | Average interest rate of credit institutions charged on loans to nonfinancial corporations and households (% p.a.) | NBR |
Interest rate on deposits | Average interest rate paid by credit institutions on nonfinancial corporations and households time deposits (% p.a.) | NBR |
Spread loan-deposit rate | Interest rate differential between loans and deposits of on nonfinancial corporations and households time deposits (% p.a.) | Authors’ calculations |
Greek fiscal crisis and banking system indicators | ||
Loss loan provisions/total loans (GR) | Loss loans provisions to total loans granted by Greek credit institutions | Bank of Greece and authors’ calculations |
Credit | Total loans granted by Greek credit institutions | Bank of Greece |
Leverage ratio | Capital/assets. Key prudential indicator | Bank of Greece and authors’ calculations |
Loans-to-deposits ratio | Loans/deposits. Proxy for liquidity | Bank of Greece |
Greek 10-year bond | Secondary market yield of the 10-year Greek government bond | European Central Bank |
Spread Greek-German bond | Spread differential between Greek and German long-term government bond yield | Authors’ calculations |
ECB Reliance Index I | Greek banks’ financing by European Central Bank to total assets of the Greek banking system. Proxy for the Greek banking system distress as a result of the fiscal crisis | Bank of Greece and authors’ calculations |
ECB Reliance Index II | Greek banks’ financing by European Central Bank to total loans of the Greek banking system. Proxy for the Greek banking system “liquidity gap” | Bank of Greece and authors’ calculations |
Unit root tests of significant variables.
ADF test statistic | ADF test statistic | Transformation | |||
---|---|---|---|---|---|
Credit risk | |||||
Loss loan provisions/total loans (R) | −2.898* | 0.049 | −3.775 | 0.021 | First differences |
Macroeconomic and cyclical indicators | |||||
Construction | −7.471 | 0.000 | −7.952 | 0.000 | First differences logs |
Gross fixed capital formation | −7.573 | 0.000 | −8.079 | 0.000 | First differences logs |
Total gross external debt/GDP | −3.026* | 0.035 | −3.228** | 0.084 | First differences |
Unemployment | −6.024 | 0.000 | −4.600 | 0.001 | None |
Total consumption | −7.855 | 0.000 | −8.247 | 0.000 | First differences logs |
CPI | −5.767 | 0.000 | −3.320** | 0.068 | None |
Trade balance | −11.400 | 0.000 | −11.398 | 0.000 | First differences |
Monetary indicators | |||||
M1 | −2.648 | 0.008 | −12.919 | 0.000 | First differences logs |
M2 | −12.393 | 0.000 | −12.590 | 0.000 | First differences logs |
Interest rates indicators | |||||
Euribor 3 month | −4.190 | 0.001 | −4.180 | 0.006 | First differences |
Romanian banking system indicators | |||||
Credit | −4.246 | 0.000 | −4.480 | 0.002 | First differences logs |
Greek fiscal crisis and banking indicators | |||||
Loss loan provisions/total loans | −5.093 | 0.000 | −5.237 | 0.000 | Second differences |
Spread Greek-German 10-year bond | −3.742 | 0.004 | −4.461 | 0.002 | First differences |
Greek 10-year bond | −2.673** | 0.082 | −3.387* | 0.058 | First differences |
ECB Reliance Index I | −8.885 | 0.000 | −9.397 | 0.000 | First differences |
*5% significance level, **10% significance level.
Serial correration test for the multivariate model.
Breusch-Godfrey LM test: | ||||
0.882585 | Probability | 0.417605 | ||
Obs * | 1.959774 | Probability | 0.375354 | |
Dependent variable: RESID | ||||
Method: least squares | ||||
Variable | Coefficient | Std. Error | Prob. | |
−2.58E-05 | 0.000457 | −0.056394 | 0.9552 | |
CPI | 1.74E-06 | 3.29E-05 | 0.052775 | 0.9580 |
UN | 5.81E-06 | 8.23E-05 | 0.070535 | 0.9439 |
DLCSTR | −0.000492 | 0.001445 | −0.340164 | 0.7346 |
DLGFC (−12) | −0.000377 | 0.001469 | −0.256693 | 0.7981 |
DTGG (−3) | 0.000669 | 0.003086 | 0.216803 | 0.8289 |
DLM2 | −0.000382 | 0.003037 | −0.125784 | 0.9002 |
DDLLPG | 0.005805 | 0.120944 | 0.047998 | 0.9618 |
DSPGD (−1) | −7.79E-05 | 0.000446 | −0.174853 | 0.8616 |
RESID (−1) | −0.157716 | 0.118716 | −1.328516 | 0.1877 |
RESID (−2) | −0.033249 | 0.120267 | −0.276463 | 0.7829 |
0.021073 | Mean dependent var | 9.21E-20 | ||
Adjusted | −0.098309 | S.D. dependent var | 0.000882 | |
S.E. of regression | 0.000924 | Akaike info criterion | −11.02404 | |
Sum squared resid | 7.01E-05 | Schwarz criterion | −10.72449 | |
Log likelihood | 523.6181 | 0.176517 | ||
Durbin-Watson stat | 1.979081 | Prob ( | 0.997482 |
The model’s specification indicates that, taken simultaneously, the CPI, the unemployment rate, the construction and the gross fixed capital formation lagged by one year, the ratio of gross external debt to GDP lagged by a quarter, M2 together with the Greek banks’ credit risk, and the spread differential between the Greek and the German sovereign debt lagged by one month explain approximately 66% of the variability of credit risk in the Romanian banking system. The relatively good fit of the regression suggests that the loss loan provisions in the Romanian banking system can be explained reasonably well by macro fundamentals and Greek crises’ indicators.
The explanatory power of the Romanian macroeconomic variables such as inflation, unemployment rate, and monetary indicators is in broad agreement with related studies [
The study is also motivated by the hypothesis that Greek-specific variables influence the Romanian banking system loan quality or alternatively the Greek crises’ matter for the neighbourhood (Anastasakis et al. [
It should be noted that caution is required when interpreting the empirical results. Any attempt should be done under the assumptions made and the time period considered. Future research would attempt to combine the proposed model with the VAR approach while accounting for the default risks at borrower level (households and corporations).
Regarding the model specification, it is worth noting that a number of studies indicate that loan loss provisions and Nonperforming loans may be noisy proxies of credit risk in the banking sector whereas other studies debate the linear functional form of credit risk. As more data become available, the proposed recommendations indicate our future work direction.
Internationally, the number of empirical studies trying to link macroeconomic factors and the asset quality of the banking sector has been growing rapidly in recent years. Addressing the high level of nonperforming loans remains a major challenge as important postcrisis effects are still unfolding.
In this study we applied time series modelling techniques to investigate the deterministic factors of Nonperforming loans in the Romanian financial system; a system dominated by foreign-owned commercial banks. Credit risk is modelled in dependence of a data-intensive set of indicators ranging from macroeconomic, monetary, financial markets and interest rates to bank-specific variables. The novelty of the study lies in the introduction of proxies for the Greek debt crisis and the Greek banks’ financial distress. Apart from the standard hypotheses investigated in the empirical literature, the study is motivated by the hypothesis that the Greek crises matter for the neighbourhood. Empirical findings suggest that macroeconomic variables, specifically the construction and investment expenditure, the inflation and the unemployment rate, and the country’s external debt to GDP and M2 together with Greek-specific variables influence the credit risk of the Romanian banking system. On the other hand, the Romanian bank-specific variables, the financial markets, and interest rates indicators were not found to possess explanatory power when added to the baseline model. Based on the findings, the hypothesis that Romanian credit risk is responsive to the country’s macro fundaments is confirmed. Furthermore, there is evidence that supports the hypothesis of linking Greek-specific indicators to the quality of the Romanian loan portfolio. The Romanian nonperforming loans seem to be adversely affected by risks arising from the Greek twin crises.
Our findings have several implications in terms of policy and regulation. Specifically, the macroeconomic significant variables identified may serve as leading indicators of credit risk deterioration. As most SEE economies rely heavily on the financial sector in terms of growth prospects, regulators should ensure financial stability by remaining vigilant on the neighbouring countries’ macroeconomic prospects and the potential contagion risk arising from the Greek banks’ subsidiaries in Romania.
For more details see Tables