Credit risk of China’s commercial banks based on the KMV model—taking 18 listed commercial banks as an example

Abstract

Credit risk constitutes a pivotal concern within the purview of commercial banking operations, commanding profound significance for regulators, the general public, and investors alike. In pursuit of a comprehensive comprehension of the intricacies of credit risk within the banking sector, this study selects a diverse cohort of 18 publicly traded commercial banks in China as its focal subjects. Employing financial and stock data from the year 2019, this research leverages the advanced KMV model to evaluate these banks’ default risks and subsequently calculate their respective default distances. The innovation of this paper lies in that we will use KMV model to quantify the credit risk level of commercial banks, and empirically study the credit risk of Chinese commercial banks through the case study of 18 listed commercial banks with different nature. The KMV model, a well-established approach for the assessment of default risk, computes the default distance predicated upon the market value’s volatility and the balance sheet’s structure, encompassing considerations of the debtor’s probability of default and default loss rate. Default distance denotes the discernible disparity between the debtor’s prevailing market value and the precipice of default, serving as a reliable metric to gauge the probability of a debtor’s default occurrence. This study meticulously curates financial and stock data pertaining to the 18 selected banks, subsequently subjecting them to rigorous evaluation through the KMV model. Through a meticulous analysis of the default distances thus derived, this research unveils the divergent spectra of default risks across banks of varying profiles, further elucidating potential risk factors. These analysis results are of important reference value for regulators and investors. Regulators can identify the potential high-risk institutions more accurately by analyzing the default distance of banks, so as to strengthen targeted regulatory measures and ensure the sound operation of the entire banking industry. At the same time, investors can also refer to these analysis results when making investment decisions to understand the credit status and risk levels of different banks, so as to better diversify risks and optimize the investment portfolio. In short, through the analysis of the default distance of banks, we can have a deeper understanding of the differences and potential risk factors in the default risk of banks of different properties, so as to provide strong decision support for regulators and investors.