On the Nexus of Credit Risk Management and Bank Performance: A Dynamic Panel Testimony from Some Selected Commercial Banks in China

This study empirically examines the liaison amid credit risk management and bank performance in a multivariate framework using bank size, non-performing loans, real GDP, net income, inflation and return of total assets to loans as indicators of credit risk and return of assets as a proxy of bank performance for some selected commercial banks in China from 2006-2017. With the application of panel econometric approaches that account for the issues of cross-sectional dependence and heterogeneity, results from the P-Y homogeneity test, Pesaran CDLM test, CIPS panel unit root test, Pedroni and Durbin-Hausman panel cointegration, the AMG estimator and the DH panel Granger causality test show that: 1) the panel time series data are heterogeneous and cross-sectionally dependent; 2) analyzed variables are integrated are of the same order (I(1)); 3) there exists a structural long-run relationship amongst the analyzed variables; 4) non-performing loan has a mitigating impact on bank performance, whereas net income and bank size have positive effect on bank performance. Real GDP and inflation impact negatively on bank performance but insignificant whilst the ratio of total assets to loans on the other hand also has a statically insignificant but positive effect on bank performance; 5) a variety of causal relationships are identified amongst analyzed variables; 6) conclusions as well as policy implications are efficient and robust since this study utilizes econometric techniques addresses the issues of heterogeneity and cross-sectional dependence.


Introduction
Management of credit is tense mainly with using the bank's resource both efficiently and lucratively to achieve preferable economic growth. Altogether, it also seeks a fair distribution among the various segments of the economy so that the economic fabric grows without any hindrance as stipulated in the national objectives, in broad, and the investment objectives in particular. Consistently credit management involves the presence of bad debts and its management. Whatever effort that credit managers put in place, there is always the occurrence of bad debts; assessment of credit management, therefore, must involve methods of debt recovery (Kumbhar, 2009). On the other hand, Madhavi & Prasad (2015), explain that financial management has a straight influence on the effectiveness of the company when management of working resources is regarded as essential in relations of liquidity and profitability. The bond between the management of credit and financial performance of banks has currently become an issue of focus that has captured a lot of theoretical debates among policymakers, academicians, and banks practitioners. First, the businessperson has the responsibility to pay the commitment due to the bank according to the specified and signed contract terms and second, the bank has an obligation to hold the businessperson in debts and to clear the businessperson of all credits after the debt has been paid (Diamond & Rajan, 2006). It is a contract requiring the fulfillment of tasks on both parties and default in those requirements by either party may have negative consequences on the connection between the parties, such as refusal to grant future loans to a defaulter (Graeber, 2011). The bank has the duty to disbursement and the debtor has the commitment to pay his/her debt. According to Kiselakova and Kiselak (2014) the management of credit portfolios is one the paramount imperative tasks for the financial institution and permanency of banking sector in linking with the increased compassion of banks credit risks and changes in the growth of values of financial instruments. In management and maximization of credit risk, the willpower of each individual loans, or borrower, risk assessment techniques plays a primary role. To determine the risk represented, it's necessary to accomplish the loan collection as a complete by an individual borrower and by respectively individual credit service.
Recent economic crunch has highlighted that a well established financial system is the basic ingredient for the economic growth. It enables an economy to be flourishing as it facilitates investors with few resources to utilize savings from those with few prospects of investing. In this regard it is crucial to now hat factors drives banking profitability. Higher profitability not only accelerate more financing to the economy it is also good for regulators as it guarantees more flexible capital ratios. Additionally higher profitability must lead to higher returns to shareholders which is the ultimate goal of the management of any bank. Journal of Financial Risk Management Despite all the above facts an financial reforms in China taken since 1990's, with an aim of improving profitability, efficiency and productivity, banks performance has still remained poor concluded by (Francis, 2010). A substantial amount of literature available shows that poor performance manifest into lower performance of bank factors including poor quality of loans, higher level of liquidity risk and higher amount o non-performing loan ratios among others. Although above said are the main hurdles affecting Chinese banks Chi & Li, (2017), demonstrated that higher government ownership ratio is the cause of risk taking in different businesses. Shih et al. (2007) examined the profitability of the big four, joint stock and city commercial banks through principal analysis and concluded that joint stock banks are better than the state owned banks. This lower profitability demonstrates the lack of competiveness in the Chinese banking sector. All these studies among others show understanding on Chinese banking sector. Thus there is strong need to explore what explains the profitability of the Chinese banking. Concerning the aforementioned issues in the banking industry of China in relation to particularly bank performance and profitability, the gap in literature with respect to bank performance in connection both internal and external factors calls for deeper investigation. Additionally, in terms minimizing the earlier posed issues such as credit risk and others in relation to bank performance, identifying the factors affecting banking performance is necessary. This current paper therefore seeks to fill the space by providing detailed analysis on the effect of credit risk indicators on bank performance in the Chinese context. Though  The rest of the paper is structured as follows: Section 2 gives brief information on the empirical literature where Section 3 focuses on the methodology of the study in general. Section 3 empirically discusses the findings whereas Section 4 summarily gives the conclusion together with some policy implications.

Credit Risk and Bank Performance
Although there has been a lot of research on the relationship between bank per- regression as an estimation method, all three parameters of credit risk (Nonperforming loans to total loan ration (NPLLR), Non-performing loans to total deposit ratio (NPLDR) and capital adequacy ratio (CAR)) were identified to have significant liaison with return of assets (ROA) and return on equity (ROE) which as proxies of bank performance. In the context of Sri-Lanka, Rasika & Sampath (2015) also in the same area of research quantitatively investigated the effect of credit risk on bank performance of commercial banks with reference to systematically important banks from 2011-2015 on a quarterly financial report.
Adopting return of equity (ROE) as a proxy of financial performance while

Data Source and Description
The study uses a panel time series data of 6 banks in China covering the period from 2006 to 2017 for the variables which includes bank size, return of assets, inflation, real gross domestic product, inflation, ratio of total asset to loans and non-performing loans. The data with respect to the aforementioned variables were obtained from audited statements of 6 banks as well as the World Bank development indicator (WDI, 2015).  Table 2 on the other hand gives the information on the descriptive statistics (mean, standard deviation, skewness kurtosis and JB test of normality) of the variables whereas Figure  The descriptive statistics as presented in Table 2   Note: All variables are transformed into natural logarithm. * and *** indicates the rejection of the null hypothesis of the Jarque-Bera test at 10% and 1% level of significance. The JB test is used to determine whether the given series follows a normal distribution or not. series to be normally distributed, the skewness must be 0 whilst the kurtosis is 3.
Having confirmed that none of the variables satisfies the conditions of normality; we therefore conclude that the series is not normally distributed. This is in line with Jarque-Bera test which rejects the null hypothesis of series being normally distributed for all variables. Therefore, the series is not normally distributed.
The study went further to test for multicolinearity to determine whether the explanatory variables used in the study are independent of each other. Table 3 illustrates the correlation matrix together with the variance inflation factor (VIF) and tolerance values for the respective independent variables. As presented, the correlation coefficients amid the independent variables are far less than 0.7. Further, the VIF and tolerance are used in checking for multicolinearity. The VIF values for independent variables are less than 0.5 whereas the values for test statistic (Tolerance) are far greater than 0.2. This as a result suggests that, there exist no issues of multicolinearity among variables within the regression model used in the study. Journal of Financial Risk Management

Model Specification
This current study estimates the relationship amid credit risk management and performance of some selected banks in China using bank size, inflation, real gross domestic product, ratio of total asset to loans and non-performing loans as measurement variables for management of credit risk in a multivariate framework. Our study in terms of variables selection is quite similar to that of Ebra- where it BP represents bank performance measured using return on assets (ROA) 1 , it CrdM is credit risk management i stands for the individual banks whilst t represents time in years. Since credit risk is measured using the measurement variables; bank size, return on assets, inflation, real gross domestic product (GDP), ratio of total loans to total assets, net income and non-performing loans Equation (1) can be reformulated as; where ROA represents return on assets used a proxy of bank performance (BP), BS is bank size, INF denotes inflation, rGDP is real gross domestic product, RTLA also mean ratio of total loans to total assets, NPL on the other hand stands for non-performing loans and NI represents net income.
1 Subsequent equations ROA (return of assets) will be used in place of BP (bank performance) since bank performance is measured using the former variable (ROA). Journal of Financial Risk Management For the purpose of econometric estimation and also since the study solely focuses on a panel data involving six (6) In order to address issues of heteroskedasticity, all the variables included in the proposed financial performance function in Equation (2)

Homogeneity and Cross-Sectional Correlation Tests
With the intention of determining whether the slope coefficients are homogenous or heterogeneous, this paper uses the Pesaran et al. (2008) The result based on the Pesaran CD LM test as well as the Pesaran-Yamagata homogeneity tests are introduced in Table 4. Considering the p-value of the CD LM test we reject the null hypothesis of cross-sectional independence at 1% level of significance, because the p-value of this statistic is found to be 0.0001 (<0.01). This therefore gives the indication that, there exist cross-sectional dependencies among series in the panel data. Furthermore, with respect to the homogeneity test using the Delta_tilde and Delta_tilde adj, the findings reveal that the null hypothesis of homogeneity is rejected also at 1% significant level indicating that, the slope coefficients are heterogeneous across all cross-sections.
We therefore conclude that, the panel time series data used for the study has cross-sectional dependence among the series and also heterogeneous. Thus the study as mentioned already employs panel data methods that are robust to cross-sectional correlations and heterogeneity.

Panel Unit Root
In order to assess the stationarity properties of the variables employed, this paper as already mentioned used the CIPS panel unit root test due to the presence of cross-sectional correlations and heterogeneity. Since the CIPS unit root test gives accurate results in the presence of both heterogeneity and cross-sectional correlation, we prefer this second generation unit root test compared to the first whereas the CADF (Cross-sectional Augmented Dicky-Fuller) unit root is estimated using the ordinary least square (OLS) approach for each ith cross-section in the panel as: where i α is a constant, t is trend, 1 t y − ∆ is the delay difference and 1 t y − is the value one term delay of t y .
Results from the second generation test (CIPS), are outlined in Table 5. The test indicates that of return of assets, real GDP, bank size, inflation, ratio of total asset to loans, non-performing loans and interest rate are not stationary at their respective levels but stationary at their first differences. Hence, the analyzed variables are integrated at the same order or in other words I(1). The panel times series data should be non-stationary at their levels so as to assess statistically and economically meaningful long-run estimates of the explanatory variables. Since all the analyzed variables are evidenced to be I(1), this study in the following section applies second-generation panel cointegration tests to identify whether or not the analyzed have a structural long-run relationship.
Given that the aforementioned panel cointegration test requires the panel time series be non-stationary at levels, the variables under discussion meet the necessary requirement.

Panel Cointegration Tests
To determine whether the regressions are spurious, the results of the panel cointegration tests must be examined. Given the results, it is appropriate to test the rho-group statistics, PP-group statistics and ADF-group statistics) and uses the following regression in Equation (9) , 1 where i ∝ and ij β are respectively intercept and slope coefficients that may vary across regions.
As shown in Table 6, the results of the Pedroni cointegration test indicate that the null hypothesis of no cointegration can be rejected by majority of the statistics at 1% significance level. Although Pedroni panel cointegration test is commonly used in literature, it has some weakness of relying on the assumption of cross-sectional dependence. Further, failure to take into account the issue of cross-sectional dependence causes loss of efficiency in identifying the cointegration relationship among variables in the panel times series data (Mensah et al., 2019). Therefore the study additionally employs Westerlund-Durbin-Hausman panel cointegration test considered as a second-generation panel cointegration test. This test takes into consideration both the issues of cross-sectional dependence and heterogeneity in identifying the cointegration among variables and thus more efficient compared to the aforementioned first generation panel cointegration test. Results from the Westerlund-Durbin-Hausman panel cointegration test are reported in Table 7. Referring to the p-values of both statistics which includes Durbin-Hausman group statistic and Durbin-Hausman panel statistic, we have evidence to reject the null hypothesis of no cointegration among return on assets, real gross domestic product, ratio of total assets to loans, non-performing loans and net income at 1% level of significance. We can therefore confidently conclude that the analyzed variables have a structural long-run relationship. More importantly, the finding of cointegration relationship among the aforementioned variables is efficient and accurate since the Westerlund-Durbin-Hausman panel cointegration test handles the problems of heterogeneity and cross-sectional correlations. Journal of Financial Risk Management

Long-Run Estimates
An important inference of an empirical study is to estimate the structural long-run parameters on the independent variables once one confirms that the level of bank size, net income, real gross domestic product, inflation and ratio of total asset to loans have a long-term relationship. Many studies with respect to literature use either the ordinary least square estimates (OLS) or the dynamic ordinary least square (DOLS) and/or fully modified OLS (FMOLS); however, these estimation methods may fail to produce efficient as well as accurate long term parameter estimates since the aforementioned estimators are not efficient to heterogeneity and cross-sectional dependencies. Given the presence of heterogeneity and cross-sectional dependence in the panel data, the study employed as mentioned already a second generation estimator that takes into account the aforementioned issues rather than OLS, DOLS and FMOLS. Thus the study employs a second-generation long-run estimator known as the Augmented Mean Group (AMG) estimator. The AMG approach follows a two-stage procedure as; µ . In Equation (10), the variable ˆt µ is included to represent the unobservable common factors evolution over time.
Results from the AMG estimator are posted in Table 8. Because the panel data are transformed into their natural logarithms, the coefficients of , , , , rGDP BS INF RTL NPL and IN are equal to the elasticities of return of assets which used as proxy of the dependent variable (bank performance) with respect to bank size, net income, real gross domestic product, inflation and ratio of total asset to loans. The AMG long-run estimation results as outlined in Table 8 shows that non-performing loans (NPL) has a mitigating impact on ROA (return of assets) measuring bank performance and also statistically significant at 1% level. This therefore gives the implication that, all things being equal 1% increase in NPL will trigger the performance of selected banks for the study to reduce or decline by 0.117% in the long-run. Contrarily, net incomes (NI) together with bank size (BS) are also identified to have a positive impact on ROA all at 1% level of significance. This therefore depicts that, all things being equal a percentage increase in both net income and bank size will increase the performance of selected Chinese banks by 0.367% and 0.134% respectively. Further, real GDP and inflation (INF) are identified to impact negatively on ROA but insignificant whilst ratio of total assets to loans (RTL) on the other hand also has a statistically insignificant but positive effect on ROA. These results are in consonant with the findings of Ebrahim et al. (2016) for the negative significant effect of NPL on ROA and the negative insignificant impact of rGDP on ROA in Yemen for the period 1998-2013, Kajola et al. (2018) for 10 banks in Nigeria from 2005-2016, Rashika & Sampath (2015 for Sri-Lanka from 2011and Isanzu (2017 for Chinese banks from 2008-2014. In the case of bank size, a positive and a statistically significant effect on ROA is evidenced at 1% level of significance. Conversely, this implies that ceteris paribus, 1% increase in the size of sampled commercial banks in China is likely to increase performance (ROA) at 0.134% in the long-run. Summarily, the estimated panel AMG model for the panel of commercial banks shows a good sign of robustness with the reason being that, results give a very substantial Wald Chi-square test value with a statistically significant probability value at 1% level. This as a result gives the implication that

Panel Causality Test
As documentation in many studies, the confirmation of long-run relationship further implies the existence of causalities among variables. The study therefore documents the Dumitrescu & Hurlin (2012)    The long-run estimated coefficients obtained from the AMG estimator undoubtedly give significant inference but does not reveal the Granger causality directions among the analyzed variables. Notwithstanding, it is of interest for authors to find out information concerning the causal liaisons amongst return of assets, real GDP, bank size, inflation, ratio of total asset to loans, non-performing loans and interest rate. Results from the Granger causality test due to Dumitrescu & Hurlin (2012) are outlined in Table 9. Evidence from the panel causality analysis gives indication that, there exist bidirectional causality between Ratio of total assets to loans (RTL) and return of total assets (ROA) at 1% level of significance. This as a result validates a feedback hypothesis between the aforementioned variables in the sense that, any increase in RTL will trigger an increase in Journal of Financial Risk Management ROA and vice versa. Also at 5% level of significance, there exist a unidirectional relationship between real GDP and return of assets (ROA) with the causation running from real GDP to ROA. This implies that real GDP in the long term has positive effect on the profitability or performance of banks select banks in China. Similarly, causation runs from non-performing loans to return of assets (ROA) at 10% level of significance and does not in the reverse sense. Inflation (INF) and non-performing loans (NPL) as well as inflation (INF) and real GDP with no doubt exhibits a unidirectional relationship running from inflation to non-performing loans and real GDP but not the opposite direction at 1% significance level. Compared to other causal relationships, another unilateral causal liaison is found extending from bank size (BS) to return of assets (ROA) and not the vice versa at 10% significance level. This may also imply that bank size is has a direct positive effect on the performance on selected Chinese commercial banks. Inte-Journal of Financial Risk Management restingly, we have enough evidence to conclude that there exist no causal relationships amid non-performing loans (NPL) and return of assets (ROA), as well as real GDP to non-performing loans (NPL), net income (NI), bank size (BS), and Ratio of total assets to loans (RTL) and vice versa. Comparatively the findings per the causalities is in consonant with that of Almekhlafi et al. (2016) who also identified a bidirectional causal relationship between Ratio of total assets to loans (RTL) and return of total assets (ROA), unidirectional causality from real GDP to ROA, as well as inflation (INF) to non-performing loans (NPL), Ratio of total assets to loans (RTL) and real GDP all in Yemen from 1990 to 2013. Results from Table 9 based on the D-H Granger causality test is graphically summarized in Figure 2.

Conclusion
This current study centered on the nexus of credit risk management and bank performance in some selected commercial banks in China from 2006 to 2017 where bank size, non-performing loans, real GDP, net income, inflation and return of total assets to loans were used to measure credit risk as explanatory variables whereas return of total assets was used as a proxy of bank performance (response variable) in a multivariate framework. The study adopted second generation panel data methods that take into account the issues of cross-sectional correlations and heterogeneity across all the banks for the analyzed variables. Consequently, the results obtained in the current study are accurate, robust and reliable. In summary, the findings and recommendations are outlined as follows:  By looking at the Pesaran-Yamagata homogeneity test for all variables within the panel data and also applying the Pesaran's CD LM test to the panel time series data, we identify the existence of heterogeneity and cross-sectional dependence across all banks for the analyzed data.  The CIPS panel unit root test indicates that the analyzed variables are nonstationary at levels but become stationary at their first differences.  The Pedroni together with Durbin-Hausman panel cointegration tests reveal that the analyzed variables are co-integrated and thus have a structural long-run relationship.  The AMG estimator in the presence of cross-sectional dependence and heterogeneity indicates that non-performing loans (NPL) has a mitigating impact on ROA (return of assets) measuring bank performance and also statistically significant at 1% level, net income (NI) and bank size (BS) also at 1% have positive effect on bank performance, whereas real GDP and inflation (INF) impact negatively on ROA but insignificant whilst ratio of total assets to loans (RTL) on the other hand also has a statically insignificant but positive effect on ROA.  The panel Granger causality test by Dumitrescu & Hurlin (2012) shows the existence of bidirectional causality between ratio of total assets to loans (RTL) and ROA (return of assets), and the presence of unidirectional causality extending from real GDP, inflation (INF), bank size (BS) and non-performing loans (NPL) to ROA, as well as inflation (INF) to real GDP.  The findings from the DH Granger causality test generally support the hypothesis that there exist significant causal relationships between the indicators of credit risk management and performance of banks in China.
Outcomes from this current study have important implications for policy makers, researchers and development partners assisting with the growth process of the banking and financial sector of China. This is due to the fact that, the roles of the banking sector is to mobilize savings, allocate resources and diversify risk.
Given that banking systems represents an important share of financial systems in China, a more efficient banking system could positively impact financial development and economic growth if banks can effectively play their financial intermediary role. This can be done if there is much sound credit risk environment and management, judicial and legal support among other considerations. Thus we recommend that, the government should strive to attain sound macroeconomic policy consistent with growth of the banking sector and prudential regulatory requirements to make banks more robust and responsive to the needs of the Chinese populace.