An Empirical Study of Credit Risk Factors of Chinese Commercial BanksTools Jiang, Yu-Kun (2022) An Empirical Study of Credit Risk Factors of Chinese Commercial Banks. [Dissertation (University of Nottingham only)]
AbstractCredit risk is one of the most significant risks faced by financial institutions. Credit risk plays an essential role in the asset management of commercial banks, and commercial banks need to establish internal credit risk management systems to avoid and control unexpected losses caused by the credit risk. However, compared with Western countries, there are gaps in credit risk management metrics and management by Chinese commercial banks, and the performance of credit risk management is uneven across banks. Given the lack of academic data and empirical analysis on post-2019 credit risk measures for listed Chinese commercial banks, the aims of this study are to examine how the credit risk of Chinese commercial banks has changed after the US-China trade dispute and the Covid-19 pandemic, how they have responded to macroeconomic alterations, and to empirically analyze the factors affecting credit risk. This dissertation measures the credit risk of 16 commercial banks from 2015-2016 using the KMV model, discusses the trend of default distance, and compares it with Moody's ratings. Moreover, six macro and micro variables are selected for panel data regression analysis with default distance to empirically analyze how each factor affects the credit analysis of commercial banks. Finally, this paper makes the following constructive suggestions for bank credit risk management. Firstly, the government ought to set up a social credit database and complete the credit evaluation system. Second, the sharing of default data by the banking industry can successfully facilitate the industry as a whole to decrease risks. Last, it is necessary for banks to be more aware of changes in the domestic and international fiscal situation and research the impact of macroeconomic alterations on the banking sector's credit risk.
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