**6. Conclusion**

Banks convert short-term assets received from depositors to long-term debt for borrowers. Therefore, banks try to maximize their expected profits by considering the risks that may arise from their activities. The concept of risk here is the state of uncertainty, which is uncertain but effective on institutional goals. Liquidity risk is one of the important risks faced by banks. Therefore, many studies on liquidity risk have been conducted in the literature. However, while assets and liabilities are two important components that constitute a bank's balance sheet, a panel study investigating long-run relation between credit expansion and liquidity risk has not been conducted in Turkey. This study aims to fill this gap in the literature. Panel cointegration approach was adopted in order to explore long-run dynamics. First, two important factors in panel methodology which are cross-sectional dependence and homogeneity were investigated properly. Pesaran [14] CD test was applied to the variables and error correction model in order to decide whether there is a crosssectional dependence between units. The null hypothesis of Pesaran [14] CD test which states that there is a dependence between units was rejected for the variables, while it was not rejected for the model. It indicates that there is no cross-sectional dependence in the residuals of error correction model. Similarly, Delta test for large and small samples were conducted in order to determine homogeneity. The null hypothesis of homogeneity was not rejected. It indicates homogeneity of constant and slope coefficients. This result shapes dynamic panel methodology structure of the study. While there is an evidence on cross-sectional dependence in the variables, cross-sectionally augmented Im-Pesaran-Shin panel unit root test was used to determine integration level of variables. One of the strengths of this test is that it takes the cross-sectional averages of the lagged levels and first differences of the individual series instead of taking difference from the estimated common factors. According to the test results, variables were found to be nonstationary. Since the first order difference of both variables was stationary, existence of the long-run relation between two variables were explored by using Westerlund's [16] paper. Four test statistics were calculated in order to decide whether there is a cointegration relation or not. The null hypothesis which shows long-run relation between variables was rejected according to the test statistics. It allows us to estimate long-run effects. Long-run relation model was estimated by using PDOLS estimator. Model was found statistically significant at 1% level. Also, coefficient of explanatory variable which is credit expansion is found statistically significant at 1% level. Sign of the coefficient is positive. It indicates positive correlation between variables. According to this correlation relation, a growth in credit expansion leads an increase in liquidity risk which affects the costs and returns of banks. This result shows importance of credit expansion on risk management. Because, uncontrolled credit expansion leads to the financial fragility of banks. This study's findings suggest that the banks may limit their credit growth strategy in order to control liqudity risk [43].

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