Interest rates, unemployment rates and exchange rates have a strong impact on both economies. However, both the French and German banking systems share two internal factors that determine credit risk: size and profitability. In addition to these common factors, the decision to lend inefficiently depends on other factors: the risk of French banks is determined by credit policy and inefficient use of capital, while the determinant of credit risk of German banks depends on the leverage of each bank. Therefore, this analysis emphasizes that credit risk is higher in a market economy than in a bank-based economy.
Thus, studies mainly use macro variables such as inflation rate, GDP growth rate, interest rate, unemployment rate, exchange rate, etc., and internal variables of banks such as capital efficiency, credit risk provision, leverage, solvency index, ROE, non-interest income and bank size to determine the impact of these variables on credit risk of the banking system. The impact of each variable may have different effects in different countries due to differences in economy, politics, society, etc. From the theoretical basis and previous studies on credit risk in the commercial banking system, a more comprehensive and general view of credit activities as well as the strategic importance in credit risk management has been provided.
Table 2.1: Summary of results of previous studies
Author
Research sample | Research results | |
Kwan and Eisenbeis (1996) | 254 bank holding companies since 2005 1986 to 1991. | Good capital efficiency can also have bad debt due to management capacity. weakness and some other reasons. |
Berger and DeYoung (1997) | US banks from 1985 to 1994. | Bad debt increases when capital efficiency decreases. |
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Commercial banks in Lithuania in 1998 | Management skills, regulations on deposit insurance and corruption impact affect bad debt | |
Ahmad and Ariff (2007) | Developed economies (Australia, France, USA) compared with emerging economies (India, Korea, Malaysia, Mexico, Thailand) in the period 1996 – 2002. | The ability to regulate capital and manage credit quality is important for credit risk. |
Berge and Boye (2007) | Nordic banking system period 1993-2005. | Bad debt is sensitive to real interest rates and unemployment rate |
Jakubík (2007) | Czech Republic banks from December 1992 to January 12/2005. | Bad debt is sensitive to real interest rates and GDP growth rates. |
Podpiera and Weill (2007) | Czech Republic Banks in the period 1994-2005. | Capital efficiency will decrease when debt bad increase |
Aver (2008) | Banks in Slovenia from 31/12/1995 to 30/11/2002. | Credit risk depends on unemployment rates, real interest rates and asset values. stock indexes |
Zribi and Boujelbène (2011) | 10 Commercial Banks in Tunisia from 1995 to 2008. | Credit risk depends on ownership structure, capital adequacy regulations & bank profits, growth rate GDP, inflation, exchange rate, real interest rate. |
Louzis and Associate (2011) | Greek banks 2003 – 2009. | Bad debt is affected by GDP growth rate, unemployment rate, interest rate, ROE, bank size, income |
excluding interest…. | ||
Castro (2013) | Greek, Irish, Portuguese, Spanish and Italian (GIPSY) Banks from Q1/1997 to Q2/1998 III/2011. | Credit risk is affected by GDP growth rate, unemployment rate, interest rate, credit growth, exchange rate exchange rate, stock index, house price |
The Last Airbender (2014) | Turkish banks from January 1998 to July 2012. | Credit risk is affected by GDP growth rate, stock index, exchange rate, unemployment rate, inflation, money supply, interest rate and credit risk. used in the previous stage. |
Chaibi and Ftiti (2015) | 147 French banks and 133 German banks from 2005 to 2011. | GDP growth, interest rates, unemployment rates and exchange rates have a strong impact on credit risk in both economies. French banks are affected by credit policy and capital efficiency. Deutsche Bank affected by leverage. |
Source: Author's synthesis
2.7 Analytical framework
CREDIT RISK
MACRO FACTORS (GDP growth rate, unemployment rate, interest rate, inflation rate, exchange rate)
INTERNAL FACTORS (operating efficiency, credit risk provisions, leverage, ROE, non-interest income, bank size)
Sources: Berge and DeYoung (1997), Hasan and Wall (2003), Louzis et al. (2011), Nkusu (2011), Zribi and Boujelbène (2011), Castro (2013), Chaibi and Ftiti (2015).
2.8 Econometric Model
From summaries of previous studies, the author proposes the following econometric model:
In the above model:
Dependent variable NPL: ratio of bad debt/total outstanding debt (representing credit risk) Independent variables:
GDP it : real GDP growth rate in year t UNE it : unemployment rate in year t
INR it : real interest rate in year t
INF it : inflation rate (consumer price index CPI growth rate) year t EXR it : VND/USD exchange rate year t
INEF it : operating cost efficiency of the i-th bank in year t LLP it : credit risk provision ratio of the i-th bank in year t LEV it : leverage ratio of the i-th bank in year t
ROE it : profitability of bank i in year t
NII it : ratio of non-interest income/total income of bank i in year t SIZE it : size of bank i in year t
U it : random error
2.9 Independent variables showing factors affecting credit risk
2.9.1 GDP growth rate:
This factor is related to the macroeconomic cycle. During the economic growth period, businesses operate effectively and production and business activities are favorable.
should have adequate capital and debt servicing capacity. Conversely, during economic downturns, the debt servicing capacity of individuals and businesses declines, which may lead to an increase in credit risk. Therefore, the relationship between GDP growth and credit risk is expected to be negative, meaning that a decline in the GDP growth rate may lead to an increase in credit risk (Fainstein and Novikov (2011); Jakubík (2007); Castro (2013)).
2.9.2 Unemployment rate:
An increase in unemployment rate will negatively affect the cash flow of households and increase the debt burden. For businesses, an increase in unemployment rate may signal a decline in production and consumer demand in the market, leading to a decrease in revenue. Therefore, the impact of unemployment rate on bad debt is expected to be positive (Castro, 2013).
2.9.3 Adjusted real interest rate:
Interest rates have a direct impact on the ability of borrowers to maintain loans and their financial capacity. An increase in interest rates leads to an increase in debt burden, thus weakening the borrower’s ability to repay and leading to an increase in bad debts (Castro, 2013; Louzis et al., 2011; Nkusu, 2011). Therefore, the relationship between interest rates and bad debts is expected to be positive.
2.9.4 Inflation rate:
Inflation has different effects on NPLs. High inflation can make debt repayment easier because inflation reduces the real value of loans. However, inflation can reduce the value of customers' real income and can also weaken their ability to repay debts. In addition, high inflation increases domestic prices, reduces purchasing power, depreciates the domestic currency, leading to increased production costs, increasing the burden on businesses. Due to different conclusions from previous studies, the relationship between inflation and credit risk can be positive or negative (Castro, 2013; Chaibi & Ftiti, 2015).
2.9.5 Exchange rate:
An increase in the exchange rate can have different effects on credit risk. An increase in the exchange rate means that the domestic currency appreciates, making domestically produced goods and services more expensive. This weakens the competitiveness of export-oriented companies in the international market, adversely affecting their revenues and ability to repay debts (Nkusu, 2011). However, an increase in the exchange rate can increase the ability of foreign currency borrowers to repay debts due to the decrease in the value of the foreign currency compared to before (Nkusu, 2011). Therefore, the relationship between exchange rates and bad debts can be either positive or negative.
2.9.6 Operating cost efficiency:
Operating cost efficiency = Total operating costs/Total operating income Berge and DeYoung (1997) showed the relationship between operating cost efficiency and total operating income.
The relationship between operating cost efficiency and credit risk is negative. The authors argue that for the “bad management” hypothesis – that is, poor operating cost management leads to increased bad debt, and the “bad luck” hypothesis – that there will be bad debts beyond the control of the bank and the bank will spend a lot of money to resolve these bad debts, leading to low operating efficiency, the relationship between operating cost efficiency and bad debt is negative. However, for the “skimping” hypothesis – that is, when banks intentionally cut costs in the short term, leading to a decrease in the quality of loans in the long term, the relationship between the two factors is positive (Berge & DeYoung, 1997). Therefore, the relationship between operating cost efficiency and credit risk can be positive or negative.
2.9.7 Credit risk provision:
The purpose of credit risk provisioning is to compensate for losses to the bank in case of customers' inability to repay their debts. Therefore, banks with many bad debts or debts with the possibility of losing capital will set up a provision level.
higher credit risk to reduce volatility in earnings (Hasan & Wall, 2003). In other words, high credit loss provisions indicate high bad debt, predicting a positive relationship between the two.
2.9.8 Leverage:
Leverage Ratio = Total Liabilities/Total Assets
Banks with high capital leverage tend to accept higher risk loans because banks need to generate high profits under low capital conditions, so the relationship between leverage and expected credit risk is positive (Chaibi & Ftiti, 2015).
2.9.9 Return on equity (ROE):
ROE = Profit after tax/Equity
The performance of the bank has a negative impact on the increase of bad debts in the future. Therefore, the relationship between ROE and credit risk is expected to be negative (Chaibi & Ftiti, 2015).
2.9.10 Non-interest income:
Non-interest income ratio = Non-interest income/Total operating income
Louzis et al. (2011) argue that non-interest income reflects the degree of diversification in the bank's business activities, leading to the diversity of non-interest income. When bad debts occur, banks can exploit these non-interest incomes to compensate for losses and partly reduce credit risk. Therefore, an inverse relationship between non-interest income and credit risk is expected.
2.9.11 Bank size:
Bank size = ln (total assets)
According to the “too big to fail” hypothesis, large banks tend to accept excessively risky loans because they are not constrained by market rules and rely on government protection in case of bank failure. Because normally, the government will support banks with large capital and influence over the financial system.
The banking system is on the verge of bankruptcy to maintain the stability of the financial system and the economy. Therefore, large banks increase their leverage and accept high-risk loans, leading to more bad debts, the impact of bank size on bad debts is predicted to be positive (Louzis et al., 2011). However, on the other hand, Zribi and Boujelbène (2011) stated that large banks have more experience in management and are able to diversify their business activities, thereby minimizing risks. Therefore, the relationship between bank size and credit risk can be positive or negative.
CHAPTER 2 SUMMARY
In chapter 2, the author presented the theoretical basis of credit risk, the factors measuring credit risk as well as reviewed previous studies on macro and internal factors of banks affecting credit risk in countries around the world such as: GDP growth rate, inflation, unemployment, interest rates, exchange rates, bank size, ROE, non-interest income, leverage, etc. It can be seen that the causes of credit risk are very diverse and credit risk itself also has a special influence on the economic environment, so the factors affecting credit risk are of special interest to researchers and bank leaders and have been widely studied in many countries. These factors will be the basis for analyzing the current state of credit risk in chapter 3 and building an empirical model in chapter 4.