Abstract
As a special type of enterprises with high risks, Chinese commercial banks’ risk management plays an important role in banks’ business process. Measuring and improving the risk management efficiency of the Chinese commercial banking system has recently attracted increasing interest. Previous studies analyze the business performance of commercial banks from the perspective of the overall management level of banks, and few articles focus on the risk management ability of banks. This paper evaluates the technical efficiencies of Chinese commercial banks’ risk management by the DEA-BCC model with window analysis to come up with some recommendations for policy makers. The technical efficiency is then decomposed into pure technology efficiency and scale efficiency. According to the banking risk supervision indicators released by the China Banking Regulatory Commission, we choose the indicators of 26 commercial banks’ risk management during the period of 2011 to 2019. Principal component analysis (PCA) is applied to delete redundant input indicators. The paper gives a dynamic evaluation of technology efficiency, pure technology efficiency, and scale efficiency. The main empirical results are as follows: (1) the technical efficiency of Chinese commercial banks’ risk management is low, and the differences among three different types of banks are large. (2) The pure technology inefficiency of Chinese commercial banks’ risk management has become a key factor restricting the improvement of the risk management of the Chinese banking industry. (3) The Chinese commercial banks’ risk management faces a serious problem which is economies of scale. (4) The technical efficiencies of Chinese commercial banks’ risk management fluctuate greatly, and management capabilities need to be enhanced urgently.
Highlights
In the past few years, the global economy has undergone tremendous changes. e international financial crisis that broke out in 2008 has aroused extensive thinking in the global financial industry. is once-in-a-century crisis shows that there are many shortcomings in the management of commercial banks, especially in the field of risk management and control, and there is still much room for development [1].Modern commercial banks have become the most important institutions which are responsible for fund collecting and distributing, and they are the hub of the entire national economy and social capital movement [2].eir business activities have an important impact on the money supply of the whole society
The state of assets and liabilities of commercial banks determines that they are a special type of enterprises with a distinctive feature of high risks, which are different from ordinary industrial or commercial enterprises [3,4]. e risks of commercial banks exist objectively, and these risks originate from all the businesses operated by the banks. e risks faced by commercial banks during their operational process mainly include liquidity risk, market risk, credit risk, operational risk, legal risk, reputation risk, and country risk [5,6]
Model, principal component analysis (PCA), and data envelopment analysis (DEA) window analysis methods to dynamically analyze the risk management efficiency of Chinese commercial banks. e selection of variables refers to the banking risk supervision indicators released by the China Banking Regulatory Commission. e results could help us explore the reasons for the low efficiency of risk management and provide some suggestions for improving the performance of Chinese commercial bank’s risk management
Summary
In the past few years, the global economy has undergone tremendous changes. e international financial crisis that broke out in 2008 has aroused extensive thinking in the global financial industry. is once-in-a-century crisis shows that there are many shortcomings in the management of commercial banks, especially in the field of risk management and control, and there is still much room for development [1]. Wang and Zhu [16] combined the idea of common boundary, revised the traditional DEA-Malmquist model, and proposed the Malmquist–Luenberger productivity index based on the common boundary They considered the impact of nonperforming loans as an undesired output. Chen et al [21] applied network DEA by considering the inputs and outputs of a bank’s surrounding production processes as additional undesirable factors and integrated the dual nature of risks to evaluate banking efficiency. Even if risk factors are considered, there are still many literature studies that only use a single indicator to replace all the risks faced by commercial banks, and the efficiency evaluation methods are not comprehensive. Model, principal component analysis (PCA), and DEA window analysis methods to dynamically analyze the risk management efficiency of Chinese commercial banks.
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