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| Countercyclical Capital Regulation of Commercial Banks in China: Selecting an Anchoring Indicator |
| WANG Qing, LIU Peng, TIAN Jiao
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Institute of Chinese Financial Studies, Southwestern University of Finance and Economics;
China Banking and Insurance Regulatory Commission;
Business School, Southwest University of Political Science and Law |
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Abstract As an important part of macroprudential supervision, countercyclical capital requirements play a primary role in alleviating systemic risks caused by excessive bank credit and promoting financial stability. Although countercyclical capital regulation has been proposed for more than eight years, China has not yet promulgated the specific regulatory rules and requirements. A key reason is the lack of both a unified specific method and time-tested indicators as a reference to establish the anchoring indicator, which is an important basis for calculating, dynamically adjusting, and releasing countercyclical capital. Should the generalized “credit/GDP” guide recommended by the Basel Committee be adopted？ Is it in line with the risk characteristics of the Chinese banking industry？ Are there any other indicators and elements of particular concern？ What is the mechanism for selecting indicators？ Discussing these issues will enrich the discussion surrounding countercyclical capital supervision and promote policy implementation in China.
This paper first reviews the countercyclical capital regulation guidance proposed by the Basel Committee and its latest implementation in various countries, and chooses 15 candidates widely used in practice, together with three classes of anchor variables proposed by the Bank for International Settlements (Drehmann et al., 2011). Considering the risk characteristics of the Chinese banking industry and data accessibility, six credit variables are added to reflect the actual loan structure in China. Altogether, these 21 candidates provide 84 anchor variables through four forms of transformation and finally yield 105 candidate indicators.
The candidate indicators are then screened and verified. First, this paper replaces “systemic financial risk/financial crisis,” which has not happened in China, with the “downward stage” of the middle-term financial cycle. Based on the latest literature, the first quarter of 2011 is set as the starting point of the “downward stage”, which serves as a reference time node for operating the countercyclical regulation. Second, by making use of the method in Drehmann and Juselius (2014), this paper draws the Receiver Operating Characteristic (ROC) curve with the data at the time of the first quarter of 2010 and respectively calculates the Area Under the Curve (AUC) value, which measures the candidate indicators' ability to predict the outbreak of systemic financial risks one year later. Third, this paper ranks each indicator by examining whether it meets the criteria of static timeliness, dynamic stability, economic interpretability, and data accessibility when used for early warning of systemic risk, so as to complete the identification process of anchor indicators. Finally, this paper carries out robustness tests by determining the starting time nodes of the “downward stage” in three financial short cycles and checking whether the ROC curve method used obtains consistent indicators in different contexts in a financial downturn period.
The empirical results show that specific industry indicators such as the trend gap of real estate price, and specific categories of credit indicators, such as the trend gap of real estate loan, the trend gap of personal housing mortgage, and the trend gap of consumer credit, can track potential risks from multiple dimensions, and thus can be used as anchor indicators for countercyclical capital supervision. In addition, the combined anchor index has a higher early warning ability and stability than single anchor indicators. These conclusions improve the precision of optimal anchor indicators to reflect systemic risk by focusing on classified credit and industry credit instead of general credit. By exploring anchor indicators and improving the early warning effect of linked variables, this paper provides practical guidelines for decision making and promoting the operation of countercyclical capital regulation in China. Its findings may also provide inspiration for other countries or regions.
This paper contributes to the literature in several ways. First, the ROC curve method is used for the first time to identify the pros and cons of China's countercyclical anchor indicators and ensure the reliability of the conclusions. Second, the candidate indicators have been comprehensively arranged and scientifically transformed, which makes it possible to select the most effective anchor indicators. Third, the period from 2004 through 2018 contains relatively complete economic and financial cycles, and thus reflects rich information and offers a basis for empirical research on countercyclical policies. Finally, this paper links countercyclical capital regulation in China to personal housing credit and consumer credit as anchor indicators, which suggests avenues for viable countercyclical capital regulation mechanisms in China.
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Received: 21 November 2018
Published: 29 November 2019
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