Macroeconomic Determinants of Credit Risk in Nepalese Banking Industry
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Proceedings of 21st International Business Research Conference
10 - 11 June, 2013, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-25-2
Macroeconomic Determinants of Credit Risk in Nepalese
Banking Industry
Ravi Prakash Sharma Poudel
This paper aims to investigate the macroeconomic determinants of credit risk
in the Nepalese banking sector by means of time series modelling. It is
motivated by the hypothesis that macroeconomic environment such as
business cycle, inflation, money supply, market interest rate and foreign
exchange fluctuation influence the banks’ credit risk which is proxied by Non-
performing Loan (NPLs). Using annual series that spam from 2001-2011,
this paper cover both the booming period and the recent financial crisis. The
findings of paper conclude that macroeconomic variables inflation and
foreign exchange fluctuation has influenced on credit risk of banks in Nepal.
The results have several implications for policymakers, regulators and
managers as the study covers the recent crisis period and also fill the gap in
research as the best knowledge the first in-depth study in the determinants
of credit risk in context of Nepal.
Key words: Credit risk, NPL, Macroeconomic variables, Banking, Nepal.
Topic: Business strategy
1. Introduction and Background
The recent financial crisis has called the interest to the cost that banking crises can
have on the economy (Agnello, Furceri et al. 2011). At the same time, it has also
motivated some economist to explore again at the determinant that may trigger
banking crises (Laeven and Valencia 2010). Macroeconomic factors are considered
to play an important role on this matter (Demirgüç-Kunt and Detragiache 1998;
Llewellyn 2002). More specifically, adverse economic condition, where growth is low
or negative, high interest and high inflation rate are favourable to banking crises
(Demirgüç-Kunt and Detragiache 1998).
A banking crisis may also take place because, in first place, banks can be under
pressure with liquidity problem caused by the increase of bad or nonperforming
loans (NPL) in their balance sheets. So, we must give concentration to the driver of
banking credit risk rather than looking at the cause of banking crisis. Credit risk is
one of the most important areas of risk management. It plays an important role
mainly for banking institution, which try to develop their own credit risk models in
order to increase bank portfolio quality. At present, minimizing and investigating the
degree of systemic risk in banking is major concern of policymakers (Demirgüç-Kunt
and Detragiache 1998). Among the various risk in bank, credit risk is the primary
cause of bank failure (Bhattacharya and Roy 2008). It has found that effective Credit
Risk Management (CRM) is essential for banking in order to minimize credit losses
(Santomero 1997). However, when putting an effective risk management in place,
some loan turns to be distress in the due course of time for various reasons. It,
therefore, understands the drivers of credit risk which is a major issue for financial
stability (Bonfim 2009). Exploring the determinants of ex post credit risk is an issueProceedings of 21st International Business Research Conference 10 - 11 June, 2013, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-25-2 of substantial importance for regulatory authorities concerned with financial stability and for banks‟ management. The ex-post credit risk takes the form of NPLs. The goal of this paper is to explore the links between macroeconomic fundamental and banks‟ NPLs in Nepalese banking sector. 2. Motivation of Study In Nepal, the commercial bank has dominant a position in the financial system and a total of 31 commercial banks are providing various facilities to the Nepalese people. Considering the various risk faced by bank, the Nepal Rastra Bank (NRB) i.e. central bank issues various guidelines and directives such as the Capital Adequacy Framework 2007, Risk Management Guidelines 2010, Corporate Governance directives are modified from time to time for commercial bank (Nepal Rastra Bank 'a' 2010). Despite the substantial progress made in terms of improving the efficiency and competitiveness related with financial system in the country, non-performing loans in some of the commercial banks is still high (Dahal 2009). Various studies Demetriades and Luintel (1996), Acharya (2003), Pokhrel (2006), Ferrari, Jaffrin et al. (2007) and Khanal (2007) related to financial and banking sector services, policies, liberalization and development has been done in the country. To the best of my knowledge, no in-depth studies have been conducted to investigate the macroeconomic determinants of credit risk in the banking industry in the country. This research intends to fill a gap in research as the first in-depth study in to the macroeconomic determinants of credit risk in the banking industry in Nepal. 3. Theoretical Framework and Hypothesis Development 3.1 Economic and Business Cycle A great deal of studies of bank risk theorized risk as creating from both of economic and business cycle. An investigation of the relationship between economic cycle and bank risk exposure shows that the relationship is dialectical (Jiménez and Saurina 2006). As business economic conditions worsen during stagnation and recession period, the riskiness of intermediation tends to rise. Banks are vulnerable to adverse selection and moral hazard behaviour of their borrowers. Koch and McDonald (2003) suggest that in good economic condition both borrowers and lender are confident about investment project and their ability to repay their loans. This encourages banks to relax credit standards and accept more risk whereas Salas and Saurina (2002) and Bhattacharya and Roy (2008) suggest the opposite. During boom periods, the economic activities in general are increasing and the volume of cash held for either businesses or households in increasing. These conditions contribute of the increased ability of borrowers to repay loans, which leads to reducing of credit risk of banks. A study conducted by Salas and Saurina (2002), Jiménez and Saurina (2006), Das and Ghosh (2007), (Gunsel 2008), Thiagarajan, Auuapan et al. (2011), (Zribi and Boujelbene 2011) and (Castro 2012) found negative relationship between Gross domestic product and non-performing loan. However, Fofack (2005) found no any relationship between gross domestic product and credit risk in Sub Sahrahan
Proceedings of 21st International Business Research Conference 10 - 11 June, 2013, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-25-2 Africa. The same result was found by Kalirai and Scheicher (2002) and Aver (2008) in case of Austrian and Slovenian banking system. 3.2 Inflation Inflation is another macro-economic factor which affects the efficiency of banking sector. Inflation depreciates the value of money which reduces the rate of return in general. High inflation rates are generally associated with a high loan interest rate. Thus, high interest rate increases cost of borrowing, which lead to an increase in the obligation of borrowers resulting in an increase in the credit risk. In the banking area, Athanasoglou, Brissimis et al. (2008) suggest that the impact of inflation on the bank profitability depends on whether the operating expenses increases at a faster rate than inflation. Since inflation reduces the future value of money, it pays people (both potential borrowers and lenders) to try to forecast inflation over the relevant time period. This forecast is called anticipated inflation (Kessel and Alchian 1962). When banks accurately forecast inflation, the management of the bank can appropriately adjust the interest rate in order to increase their revenues faster than the cost which mitigates the negative impact of inflation. Thiagarajan, Auuapan et al. (2011) examined the relationship between current inflation and one year lag inflation with credit risk and found positive relationship between current inflation and credit risk and no any relationship between one year inflation lag and credit risk in case of public sector banks but the result showed that there is no any relationship between inflation and credit risk in case of private sector banks. Similarly, Gunsel (2008) and Rinaldi and Sanchis-Arellano (2006) examined the influence of inflation to credit risk in North Cyprus and Euro Zone country respectively and found positive relationship. In the opposite direction, (Shu 2002), Zribi and Boujelbene (2011) and Vogiazas and Nikolaidou (2011) in case of Honkong, Tunsian and Romanian banking sector, found negative relation between inflation and credit risk. However some other study by Aver (2008), Bofondi and Ropele (2011) and (Castro 2012) in case of Solvenian, Italian, and GIPSI banking system, didn‟t find any influence of inflation to credit risk. 3.3 Money Supply The relationship between money supply and credit risk appears through the behaviour of borrower resulting from change in money supply in the economy. However, if the central bank decides to follow expansionary monitory policy, it lowers the required reserve rate and reduces the discount rate. This increase money supply, which means increase productivity and profitability which in turn stimulates investment and consumption. As a result, income increases. Moreover, increasing money supply will decrease an interest rate and increase the opportunity of public to have cheaper fund. These conditions increase the ability of borrowers to pay back their obligations and contribute in decreasing the banks‟ exposure to credit risk (Ahmad and Ariff 2007). Accelerating money supply growth can act as an indicator of future growth potential (Berk and Bikker 1995). The impact of money supply on credit risk was examined by Ahmad (2003). She examined factor contributing to risk formation in 65 deposit taking institution in Malaysia. She found a significant and negative relationship between M3 as a proxy of money supply and credit risk. Similar
Proceedings of 21st International Business Research Conference 10 - 11 June, 2013, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-25-2 result was found by Kalirai and Scheicher (2002) and Vogiazas and Nikolaidou (2011) in Austrian and Romanian banking system. In the opposite direction Bofondi and Ropele (2011) found positive relationship between money supply and credit risk in Italian banking system. However, Fofack (2005) found no any influence of money supply in credit risk. 3.4 Market Interest Rate The interest rate is another important conditioning of the credit risk because it affects the debt burden. This means that the effect of the interest rate on the credit risk in expected to be positive. In fact, the increase in the debt burden caused by rising interest rates will lead to a higher rate of nonperforming loan (Aver 2008; Louzis, Vouldis et al. 2011; Nkusu 2011). A rise in market interest rates, whose direct effect is an increase in bank return for newly made or variable interest loans, nonetheless bears a danger of increased credit risk. In the light of asymmetric information theories, higher interest rates tend to exacerbate the problem of adverse selection- that is, in the context of credit relationships, the selection of borrowers with high probability of adverse project outcomes, or “bad risk”. Richard (1999) found a significant and negative relationship between real interest rate measured by nominal interest rate on three year treasury notes minus the inflation rate and bank failure similar result was found by Fofack (2005) in Sub- Sarahan Africa and found positive relationship between real interest rate and credit risk. This suggests that the rising interest rate to the extent that the increase of the cost of deposits at the commercial banks may have contributed to a decrease in the banks‟ profit. On another hand, Jiménez and Saurina (2006) used interbank interest rate to measure the impact of interest rate on problem loans. They found a significant and positive relationship between problem loans and interest rate. The same relationship was found between the interest rate measured by ten year Italian Treasury bond and the loan loss provision by (Quagliariello 2007). Castro (2012) conducted study in GIPSI (Greece, Ireland, Portugal, Spain and Italy) from 1997 to 2011 and found positive relationship between long term interest rate and credit risk. This supports the idea that high interest rate increases the obligation of borrowers and thus increases the banks‟ credit risk. Ali and Daly (2010) found no any significant relationship between short-term interest rate and credit risk in Australia. 3.5 Exchange Fluctuation Exchange rate is also one of macroeconomic debates in the developing markets and volatility of exchange rate is one of the main sources of economic instability (Zameer and Siddiqi 2010). Exchange rate measures the relative worth of domestic currency in terms of another (Zameer and Siddiqi 2010). The main problems the firms face are the frequent appreciation of foreign currencies against the local currency, and the difficulty in retaining local customers because of the high prices of imported inputs which tend to affect the prices of their final products sold locally (Sirpal 2009). As the domestic price of foreign exchange rate rises (depreciated) it becomes more expensive to procure foreign product and services as their cost would have
Proceedings of 21st International Business Research Conference 10 - 11 June, 2013, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-25-2 increased thereby requiring more units of domestic currency to acquire the same quantity of foreign goods and services than before. This results in an increase in the demand for bank credit to support finances for covering the additional expenditure required as a result of exchange rate depreciation (Ngerebo 2011) and reduce the firm‟s profitability. If the firm‟s profitability decreases, then firm face the problem to serve interest and principal of debt. A real depreciation is expected to have expansionary effects by increasing the operating profit in the export sector but lead to a contraction in the import sector due to opposing reasons (Nucci and Pozzolo 2001). Contrarily, large currency depreciation may deteriorate the firm‟s net worth through the „balance sheet-effect‟, as the dollar-denominated debt burden of firms increase (Pratap and Urrutia 2004). Castro (2012) conducted study in GIPSI (Greece, Ireland, Portugal, Spain and Italy) from 1997 to 2011 and found negative relationship between real effective exchange rate and credit risk. Zribi and Boujelbene (2011) conducted study in Tunisia and used ratio of risk weighted assets to total assets as proxy of credit risk and found negative relationship between exchange rate and credit risk and same result was found by Gunsel (2008) in North Cyprus. Some researcher Kalirai and Scheicher (2002) and Aver (2008) in Austria and Slovenia have not found any relationship in foreign exchange fluctuation and credit risk. Vogiazas and Nikolaidou (2011) found real effective exchange rate with three quarter lag is negatively related with credit risk in Bulgaria from 2001 to 2010 and similar result was found by Fofack (2005) as well. Based on above literature, the hypothesis is developed as follows: H1. Gross Domestic Product Growth is negatively related with credit risk. H2. Inflation rate is positively related with credit risk. H3. Broad Money Supply Growth is negatively related with credit risk. H3. Market Interest Rate is positively related with credit risk. H4. Foreign Fluctuation is negatively related with credit risk. 4. Methodology Time series and cross sectional data has been used in this study where 29 commercial banks out of 31 banks have been included in the study in Nepal over the period of 2001-2011. The credit risk of bank is the dependent variable and other five are independent macroeconomic variable. Finally, this study also include two control variable i.e. credit to deposit ratio and capital adequacy ratio. Descriptive and multiple regressions are the methods of analysis in the study. The complete model is as: Credit Risk = β0 + β1GDP + β2INF + β3M2 + β4IBR + β5EXHG+ β6 CDR + β7CAR + eit Where, Credit Risk = Ratio of NPLs to total loan at the end of each year.
Proceedings of 21st International Business Research Conference
10 - 11 June, 2013, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-25-2
GDP = Annual Gross Domestic Product growth rate.
INF = Inflation rate (consumer price index).
M2 = Broad Money Supply growth rate.
IBR = Interbank rate.
EXHG = Exchange rate fluctuation
CDR = Credit to total deposit ratio.
CAR = Capital Adequacy Ratio
5. Results and Discussion
5.1 Descriptive Statistics
Table 1
N Minimum Maximum Mean (%) Std. Deviation
(%) (%) (%)
NPL 187 0 11.7 2.49 2.83
GDP 193 3.4 6.1 4.4 0.79
INF 207 2.7 11.6 7.7 3.07
M2 207 2.7 38.8 16.78 10.41
IBR 207 0.71 8.2 4.42 2.08
EXCG 207 -14 12 -0.25 7.36
CDR 207 31.63 160.6 79.74 17.92
CAR 207 -15.11 133.80 14.49 12.37
The descriptive statistics for the dependent, independent and control variable are
provided in Table 1. The mean value of NPLs is 2.49.% which are ranged from
minimum 0% to maximum 11.7%. The mean of GDP and INF is 4.4% and 7.7%
respectively which is ranged from minimum 3.40% to 6.1% in case of GDP and in
case of INF it is from 2.7% to 11.6%. Similarly the mean value of M2 and IBR is
16.7% and 4.42% respectively and statistics show these two variables are also in
wide range. The mean value of EXCG rate is -.25% where the maximum and
minimum is 12% and 14% respectively. The statistics show higher mean value of
CDR which is 79.7% than all other variable. The average value of CAR reflect that
the bank they have maintain the capital an average upto 14.5%.Proceedings of 21st International Business Research Conference
10 - 11 June, 2013, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-25-2
5.2 Regression Results
Table 2
Independent Coefficient T statistics p value
Variable
GDP .030 .290 .772
INF -.443 -3.241 .001
M2 -.085 -.503 .616
IBR .063 .427 .670
EXCG -.239 -2.319 .022
CDR .046 .608 .544
CAR -.215 -2.896 .004
R Square 0.18
Adjusted R Square 0.15
F Statistics 5.302
Number of 175
observation
The table 2 contains the beta coefficients of five macro economic variables and two
control variables. The beta coefficient is indicators of the predictive power of the
individual variables. The entire beta coefficient is negative implying an inverse
relationship between the dependent variable and the independent variable and vice
versa.
The regression result of GDP shows the coefficient estimate is positive however
statistically not significant which shows that there is no any significant relationship
between GDP growth and non-performing loan which is consistent with the findings
of Kalirai and Scheicher (2002) and Aver (2008). Thus first hypothesis that GDP is
negatively related to the credit risk is not accepted. As per result, it can be explain
that during recession periods, the banks tend to be more cautious in selecting
borrowers and in assessing credit conditions. Hence, they decrease the volume of
credit. But in fact, the bank experience non-performing loans that have been already
distributed during boom conditions (Jimenez and Saurina, 2006).
The negative statistically significant value of inflation suggests that inflation has a
substantial impact on credit risk. This not was we expected, the negative sign of the
inflation coefficient suggest there is a negative relationship between inflation and
credit risk which is consistent with the findings of Shu (2002), Zribi and Boujelbene
(2011), Vogiazas and Nikolaidou (2011). Thus, the second hypothesis that inflation is
positively related to the credit risk is not accepted. The result explains that during a
high inflation period, the bank not intends to disburse long term loan and they insist
the lending only in assured sectors in the economy. This process decrease the loan
volume and the banks become more selective of high quality borrowers which
decrease the bank‟s credit risk.
Money supply was found to be negative but not significantly related to the credit risk
which is consistent with the findings of Fofack (2005) who found no any relationship
between money supply and credit risk. Thus, the hypothesis, broad money supply isProceedings of 21st International Business Research Conference 10 - 11 June, 2013, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-25-2 negative related with credit risk is not accepted. Previous studies which found a negative relationship suggest that the high growth of the money supply leads to reduce the interest rate; as a result the borrowers will have a cheap fund, which contributes to an increase in their ability to repay their financial obligations. Market interest rate was found to be positive but not statistically significant to the credit risk. Previous studies by Jimenez and Saurina (2006) shows that the interbank interest rate is positively related with the credit risk which is not found in this study. This finding is consistent with the findings of Ali and Daly (2010) who found no any relationship between interest rate and credit risk in Australia. Thus the fourth hypothesis that the market interest rate is positively related with credit risk is not accepted. The negative statistically significant value of foreign exchange suggests that there is negative relationship between foreign exchange and credit risk which is consistent with the findings of Gunsel (2008), Zribi and Boujelbene (2011) and Castro (2012). The results suggest that increase in exchange rate i.e. appreciation of the local currency making the goods and services produced in that country relative more expensive. This weakens the competitiveness of export-oriented firms and affects adversely their ability to service their debts. Thus the fifth hypothesis that fluctuation of exchange rate and credit risk is negatively related is accepted. 6. Conclusion and Implication The recent financial crisis has revived the interest on the analysis of the problem that banking crises can have over the economy and on the factors that may trigger a banking crisis. However, before looking at the causes of banking crisis, we should give some attention to the conditionings of the banking credit risk. In reality, before a banking crisis arises, banks can be struggling with liquidity problems caused by the increase NPL in their balance sheets. Thus, to understand the origin of banking crises, it is necessary starting by considering the factors that affect banking credit risk in first place. Several studies have focused their attention on this matter and have concluded that the macroeconomic environment has strong influences on banking credit risk. This paper has analysed deeply the link between the macroeconomics and banking credit risk in Nepal. Employing regression times series cross sectional data approaches over the period from 2001-2011. This study found that banking credit risk is significantly negatively affected by inflation and foreign exchange fluctuation. However, other macroeconomic variable GDP growth, Broad Money Supply growth, Market Interest Rate has no any influence in credit risk in Nepalese banking industry. The results have several implications for policymakers, regulators and managers as the study covers the recent crisis period. The result of the study of macroeconomic determinants of credit risk in Nepal can also be beneficial to banks in other countries in transition which is still in process of applying the latest credit risk measurement and management methods. The results of analysis are analysis are also applicable to other financial institutions such as insurance companies in risk management of their financial investment. Based on the study other macroeconomic factors not
Proceedings of 21st International Business Research Conference
10 - 11 June, 2013, Ryerson University, Toronto, Canada, ISBN: 978-1-922069-25-2
studied in this research has very significant contribution of 85% to banks‟ credit risk
therefore require further research to explore the other macroeconomic determinants
of credit risk.
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