The purpose of this article is to analyze the effects of Romania's macroeconomic variables of the loan portfolio quality of the banking sector. Specifically, the study seeks to emphasize the interdependent macroeconomic elements that influence the evolution of credit portfolio quality for commercial banks. To achieve these correlations we use both the VAR model and the method of least squares. Monetary and structural influences are highlighted by using cumulative impulse – answer functions. The results show that monetary factors have contributed greatly to the intensity of financial crises. Beyond these results, it can be concluded that the interest rate and real exchange rate play an important role in sizing the loan portfolio quality at the banking system level

Alina Georgiana Manta

Roxana Maria Bădîrcea

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96                                                                      Finance – Challenges of the Future Case – study Concerning the Effects of the Macroeconomic Variables on the Loan Portfolios Quality of the Romanian Banking Sector Using the VAR Model and Least Squares Method  Alina Georgiana Manta1, Roxana Maria Bădîrcea2 1,2 University of Craiova, Faculty of Economics and Business Administration iacobescu_a@yahoo.com, roxanamariapirvu@yahoo.com  Abstract:  The  purpose  of  this  article  is  to  analyze  the  effects  of  Romania's macroeconomic  variables  of  the  loan  portfolio  quality  of  the  banking  sector. Specifically,  the  study  seeks  to  emphasize  the  interdependent  macroeconomic elements that influence the evolution of credit portfolio quality for commercial banks. To achieve these correlations we use both the VAR model and the method of least squares.  Monetary  and  structural  influences  are  highlighted  by  using  cumulative impulse – answer functions. The results show that monetary factors have contributed greatly to the intensity of financial crises. Beyond these results, it can be concluded that the interest rate and real exchange rate play an important role in sizing the loan portfolio quality at the banking system level.  Keywords: interest rate, economic growth, exchange rate, credit risk rate.  JEL Classification:  E32, G14.  1.  Introduction  Along time, the bank crisis affected many countries and led to the bankruptcy or restructuring of many credit institutions. The solvability of a credit institution resides in the quality of the loan portfolio, the risk exposure being thus a key indicator of the financial vulnerability of a bank. This  subchapter  focuses  on  the  quantification  of  the  effects  of  Romania’s macroeconomic performances on the quality of the loan portfolio from the bank sector. To be more specific, the study tries to discover those independent macroeconomic elements (such as the interest rate, the increase of the GDP, the exchange rate) which influence the evolution of the quality of the loan portfolio for the commercial banks. In order to achieve these correlations we will use the VAR model and the least squares method.  2.  Research and Methodology   Therefore, we will apply the VAR model and the impulse-response analysis in order to set the causality relations between the economic variables and the loan quality variables. Plus, similarly to the model proposed by Baboucek and Jancar (2005), the scenario analysis and the stress test can also be applied, in order to examine their impact on the quality of the loan portfolios of the banks in Romania. The stress tests are performed within some exceptional events, but plausible both with hypothetic and also  historical  character  in  order  to  evaluate  the  vulnerability  of  loan  portfolios  to negative factors at macroeconomic level. The idea of these simulations is to offer a Year XIII, No. 15/2013                                                                                                97 future  oriented  evaluation  of  the  bank  sector  on  credit  risk  exposure  level  for  the purpose of maintaining a financial stability.  The VAR model uses a linear equations system to catch the dynamic of the feedback  relations  between  two  or  more  endogenous  variables.  VAR  treats  all  the variables  as  symmetric,  without  supposing  that  a  variable  is  independent  and dependent.  All  the  endogenous  variables  are  affected  by  the  present  and  past achievements of those variables. The structural form of the model is as follows (Tracey Marlon, 2008):  1 ...... ... ... ...... 1... 12 12 211 12n nnnb bb bb bntttyyy...21 = n aaa...21 +                      L L LL L LL L Lnn n nnn...... ... ... .........2 12 22 211 12 1111 21 1...ntttyyy + nttt...21(1) or  in a more compact form:  t y  =   +    L 1  t y  +  t  (2)  where  B  is  a  matrix  n n   of  the  coefficients  of n   endogenous  variables  in  the t y vector.  A  represents the constant vector  1  n ,    L   is the matrix  n n  of the polynomial spreads which catches the spreads of the endogenous variables, and  t   is the vector  1  n ,  t  ~     , 0 N . The model from equation [2] can be adjusted in order to include exogenous variables as:  t y  =   +    L 1  t y  +  t x + t  (3)  In this case,   is a matrix  p n  of the coefficients and  t x  is the vector  1  p  for  the  exogenous  variables  such  as  the  weather  and/or  an  accidental  variable.  Therefore, the VAR model avoids this problem of endogeneity, estimating the model through  a  simplified  form,  in  accordance  to  the  predetermined  and  the  residual variables. Multiplying the equation [3] by 1  B  a form reduced VAR results:    t t t t e x C y L C C y      2 1 1 0  (4)  Where:  A B C10  ,      L B L C  11   12 B C  şi  t t B e 1   . As  t e  is a function of  t  , it is made of uncorrelated residual values which will be correlated during the equations.      98                                                                      Finance – Challenges of the Future 3.  Empirical Results  The study considers the period during 2000 – 2011 and one used the quarterly data  provided  by  the  European  Central  Bank  and  the  National  Bank  of  Romania. These data are represented in table no.  1. The endogenous variables used for the VAR model are: the credit risk rate (RRC), interest rate (INT), the GDP growth rate (G) and the real exchange rate (REER). The exogenous variable is considered constant. To assure accurate results, the logarithms of the data introduced in the Eviews statistic program have been previously found, except for the G variable.  In scenario no. 1 one presents the results of the estimates concretised in the responses of the credit risk rate to the GDP increase rate shocks, of the real exchange rate and of the interest rate on our country level. From scenario no. 1 and graphic no. 1 results  the  fact  that,  in  the  case  of  credit  institutions,  the  improvement  of  the  loan portfolio quality of a bank is due to a depreciation of the real exchange rate, while a high interest rate leads to the increase of the non-reimbursement probability risk. At the same time, the increase of the GDP only increases the incomes of the population and implicitly to minimize the credit risk.   Table 1. The variable used in the model (only annual values)    RRC  INT  G  REER 2000  3.83  35  2.4  95.55 2001  2.54  35  5.7  94.12 2002  1.1  27.7  5.1  77.05 2003  3.37  19.03  5.2  78.13 2004  2.85  19.925  8.5  74.67 2005  2.61  8.625  4.2  100 2006  2.81  8.6175  7.9  107.16 2007  3.99  7.3275  6.3  128.47 2008  6.52  9.8125  7.3  138.76 2009  15.29  9.095  -7.9  120.69 2010  20.82  6.5  -1.3  128.67 2011  23.28  6.25  2.5  130.31 Source: European Central Bank and National Bank of Romania  Scenario no. 1. Estimation of the VAR model for Romania   Vector Autoregression Estimates      Date: 03/20/13   Time: 03:40      Sample (adjusted): 2000Q3 2011Q4      Included observations: 46 after adjustments   Year XIII, No. 15/2013                                                                                                99  Standard errors in ( ) & t-statistics in [ ]                       LRRC  LINT  G  LREER                   LRRC(-1)   1.452757   0.037035  -2.139400  -0.050623    (0.16276)   (0.09989)   (1.83030)   (0.03740)   [ 8.92550]  [ 0.37076]  [-1.16888]  [-1.35351]          LRRC(-2)  -0.549281  -0.054274   1.492734   0.046842    (0.16011)   (0.09826)   (1.80044)   (0.03679)   [-3.43067]  [-0.55237]  [ 0.82910]  [ 1.27321]          LINT(-1)  -0.123321   1.350712   4.735979   0.165779    (0.43545)   (0.26723)   (4.89665)   (0.10006)   [-0.28320]  [ 5.05448]  [ 0.96719]  [ 1.65679]          LINT(-2)   0.013063  -0.344356  -5.642006  -0.203549    (0.44143)   (0.27090)   (4.96392)   (0.10143)   [ 0.02959]  [-1.27115]  [-1.13660]  [-2.00670]          G(-1)  -0.000453   0.004876   1.094165  -0.007274    (0.01904)   (0.01168)   (0.21410)   (0.00437)   [-0.02382]  [ 0.41727]  [ 5.11054]  [-1.66257]          G(-2)  -0.004630  -0.004435  -0.405405   0.004755    (0.01615)   (0.00991)   (0.18156)   (0.00371)   [-0.28676]  [-0.44761]  [-2.23290]  [ 1.28154]          LREER(-1)  -0.754544   0.137292   10.72132   1.707168    (0.94079)   (0.57735)   (10.5792)   (0.21618)   [-0.80203]  [ 0.23780]  [ 1.01343]  [ 7.89699]          LREER(-2)   0.836047   0.059982  -14.29626  -0.847704    (1.00496)   (0.61674)   (11.3009)   (0.23093)   [ 0.83192]  [ 0.09726]  [-1.26505]  [-3.67088]          C   0.095930  -0.932528   21.28729   0.774475    (1.09925)   (0.67460)   (12.3611)   (0.25259)   [ 0.08727]  [-1.38235]  [ 1.72211]  [ 3.06612] 100                                                                      Finance – Challenges of the Future                    R-squared   0.976638   0.979806   0.877156   0.977768  Adj. R-squared   0.971586   0.975439   0.850595   0.972961  Sum sq. resids   0.927446   0.349291   117.2772   0.048970  S.E. equation   0.158323   0.097161   1.780352   0.036380  F-statistic   193.3440   224.4005   33.02425   203.4074  Log likelihood   24.51997   46.98014  -86.79684   92.16794  Akaike AIC  -0.674781  -1.651311   4.165080  -3.615997  Schwarz SC  -0.317004  -1.293533   4.522858  -3.258220  Mean dependent   1.573769   2.530689   3.944022   4.646810  S.D. dependent   0.939249   0.619974   4.605988   0.221244                     Determinant resid covariance (dof adj.)   1.30E-07      Determinant resid covariance   5.44E-08      Log likelihood   123.6441      Akaike information criterion  -3.810612      Schwarz criterion  -2.379501                       Estimation Proc: =============================== LS 1 2 LRRC LINT G LREER  @ C  VAR Model: =============================== LRRC = C(1,1)*LRRC(-1) + C(1,2)*LRRC(-2) + C(1,3)*LINT(-1) + C(1,4)*LINT(-2) + C(1,5)*G(-1) + C(1,6)*G(-2) + C(1,7)*LREER(-1) + C(1,8)*LREER(-2) + C(1,9) VAR Model - Substituted Coefficients: =============================== LRRC = 1.452757433*LRRC(-1) - 0.5492805802*LRRC(-2) - 0.1233207335*LINT(-1) + 0.01306262589*LINT(-2) - 0.0004534614336*G(-1) - 0.004629980509*G(-2) - 0.7545439414*LREER(-1) + 0.8360465674*LREER(-2) + 0.09592981381  Source: own calculations in Eviews program  We present in the annex no.1 the response functions of the risk credit indicator to  the  GDP  shocks,  the  interest  rate  and  the  real  exchange  rate.  Thus,  a  positive response  is  associated  to  an  increase  of  the  incomes  of  the  population  and  thus implicitly to a decrease of the credit risk. On the other hand, a negative shock of the real exchange rate is associated to a decrease in the loan portfolio quality, because the debtor purchase power increases. At the same time, a positive shock of the interest rate also has a negative impact on the loan portfolio quality because thus the rates to pay for the debtors increase and thus the non-reimbursement risk increases.  In the case where we use the least squares method, we obtain the following results:   Year XIII, No. 15/2013                                                                                                101 Scenario no. 2. Estimation of the Least Squares Method for Romania  Dependent Variable: LRRC     Method: Least Squares     Date: 03/20/13   Time: 03:47     Sample: 2000Q1 2011Q4     Included observations: 48                        Variable  Coefficient  Std. Error  t-Statistic  Prob.                      G  -0.090279  0.017011  -5.307092  0.0000 LINT  -0.322741  0.173772  -1.857270  0.0700 LREER  1.723590  0.523872  3.290096  0.0020 C  -5.259882  2.791466  -1.884272  0.0661                    R-squared  0.722640     Mean dependent var  1.566207 Adjusted R-squared  0.703729     S.D. dependent var  0.919781 S.E. of regression  0.500644     Akaike info criterion  1.533811 Sum squared resid  11.02834     Schwarz criterion  1.689744 Log likelihood  -32.81147     F-statistic  38.21292 Durbin-Watson stat  1.703652     Prob(F-statistic)  0.000000                    The estimated regression equation is:  LRRC = -0.09*G - 0.32*LINT + 1.72*LREER - 5.25 (5)  From  the  above  equation  results  that  there  is  a  high  intensity  negative correlation between the GDP increase rate and the credit risk which reflects the fact that  at  a  decrease  by  a  percent  of  the  GDP  increase  rate,  the  credit  risk  rate  will increase by 0.09%. On the other hand, there is a negative correlation between the monetary policy credit risk rate and the credit risk rate, namely at a decrease by a percent of the interest rate, the credit risk rate will increase by 0.32%. However, in the case of the effective real exchange rate, there is a positive correlation with the credit risk rate, so that at an increase by a percent of the actual real exchange rate, the credit risk rate increases by 1.72%. By analysing the p-value registered values one can say that only in the case of the GDP increase rate and of the actual real exchange rate the obtained results are significant because the obtained values are much smaller than the 0.05% significance threshold. In the case of the monetary policy interest rate, the p-value is placed over the minimum significance threshold, and therefore it results that one accepts the null hypothesis and we cannot achieve any correlation between the interest rate and the credit risk rate. In other words, in the case of the method of the smallest  squares,  the  monetary  policy  interest  rate  would  exercise  no  significant influence on the credit risk rate. The value of R2 shows that approximately 72% of the credit  risk  rate  variation  is  explained  by  the  variation  of  the  variation  of  the  GDP increase rate, of the actual real exchange rate and of the monetary policy interest rate.  Therefore  we  have  obtained  results  in  the  case  of  both  used  method  which denotes that the estimated model is correct and the obtained results are significant.  102                                                                      Finance – Challenges of the Future 4.  Conclusions  This  article  points  out  thus  the  impact  of  some  moderate  and  external macroeconomic  shocks  on  the  quality  of  the  loan  portfolios  of  banks.  The  VAR methodology offers us useful results for research. Thus, the monetary and structural influences  are  pointed  out  through  the  cumulative  use  of  the  impulse-response functions. The monetary factors have greatly contributed to the intensity of the financial crises. Beyond these results, it is obvious that the interest rate and the real exchange rate play an important role in  the  dimensioning of the loan  portfolio at the banking system level. Therefore,  the  monetary  authorities  must  be  careful  when  they  use  the exchange rate as monetary policy instrument considering the fact that the impact on the exposure to the credit risk is not homogenous at the level of all credit institutions. The increase of the interest rate and a high inflation represent early warning systems of  the  deterioration  of  the  loan  portfolio  quality.  In  conclusion,  the  govern representatives  and  the  banks  must  efficiently  administrate  the  risk  in  favourable economic conditions.   Annex no. 1. The responses of the credit risk rate to the shocks to the GDP increase rate, the interest rate and the real exchange rate shocks -.4-.3-.2-.1.0.1.2.3.41 2 3 4 5 6 7 8 9 10Response of LRRC to LRRC-.4-.3-.2-.1.0.1.2.3.41 2 3 4 5 6 7 8 9 10Response of LRRC to LINT-.4-.3-.2-.1.0.1.2.3.41 2 3 4 5 6 7 8 9 10Response of LRRC to G-.4-.3-.2-.1.0.1.2.3.41 2 3 4 5 6 7 8 9 10Response of LRRC to LREER-.2-.1.0.1.2.31 2 3 4 5 6 7 8 9 10Response of LINT to LRRC-.2-.1.0.1.2.31 2 3 4 5 6 7 8 9 10Response of LINT to LINT-.2-.1.0.1.2.31 2 3 4 5 6 7 8 9 10Response of LINT to G-.2-.1.0.1.2.31 2 3 4 5 6 7 8 9 10Response of LINT to LREER-3-2-101231 2 3 4 5 6 7 8 9 10Response of G to LRRC-3-2-101231 2 3 4 5 6 7 8 9 10Response of G to LINT-3-2-101231 2 3 4 5 6 7 8 9 10Response of G to G-3-2-101231 2 3 4 5 6 7 8 9 10Response of G to LREER-.08-.06-.04-.02.00.02.04.061 2 3 4 5 6 7 8 9 10Response of LREER to LRRC-.08-.06-.04-.02.00.02.04.061 2 3 4 5 6 7 8 9 10Response of LREER to LINT-.08-.06-.04-.02.00.02.04.061 2 3 4 5 6 7 8 9 10Response of LREER to G-.08-.06-.04-.02.00.02.04.061 2 3 4 5 6 7 8 9 10Response of LREER to LREERResponse to Cholesky One S.D. Innovations ± 2 S.E. Source: own calculations in Eviews program Year XIII, No. 15/2013                                                                                                103 References  Babouček, Ivan and M. Jančar (2005) “Effects of Macroeconomic Shock to the Quality of the Aggregate Loan Portfolio”, Czech National Bank, Working Paper Series, No. 1, p. 1-62. Berger  Allen  N.,  DeYoung  Robert  (1997)  ”Problem  loans  and  cost  efficiency  in  commercial banks”, Journal of Banking & Finance 21 (1997) 849-870. Fernández de Lis Santiago, Martínez Pagés Jorge and Saurina Jesús (2000) ”Credit Growth, Problem  Loans  and  Credit  Risk  Provisioning  in  Spain”,  Banco  de  España  —  Servicio  de Estudios Documento de Trabajo n.º 0018. Karim Mohd Zaini Abd, Chan Sok-Gee, Hassan Sallahudin (2010) ”Bank Efficiency and Non-Performing Loans: Evidence from Malaysia and Singapore”, Prague Economic Papers, 2. Opriţescu  Marin,  Manta  Alina,  Perpelea  Mircea  (2010)  ”Quantifying  the  effects  of  the macroeconomic variables on the loan portofolio quality for the Romanian banking system using the VAR model”, Revista Finance - Challenges of the Future, nr.11. Tracey Marlon (2008) ”A VAR Analysis of the Effects of Macroeconomic Shocks on Banking Sector Loan Quality in Jamaica”, Bank of Jamaica. Younus Sayera (2007) ”Nexus among Output, Inflation and Private Sector Credit in Bangladesh”, Bangladesh Bank. Banca Naţională a României (2012) Raport asupra Stabilităţii Financiare. Fondul Monetar Internaţional (2008) Regional Economic Outlook, Europe, Reassessing Risks. Banca Centrală Europeană - Statistical Data Warehouse.  

Case – study Concerning the Effects of the Macroeconomic Variables on the Loan Portfolios Quality of the Romanian Banking Sector Using the VAR Model and Least Squares Method

Case – study Concerning the Effects of the Macroeconomic Variables on the Loan Portfolios Quality of the Romanian Banking Sector Using the VAR Model and Least Squares Method

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