Summary:
Since the global financial crisis of 2008, a major event in China's financial system has been the ascent of shadow banking. Consisting of bank wealth management business, inter-bank business, trust loans, entrusted loans, and various asset management products, so-called shadow banking is rapidly expanding in scale, has an increasingly complex transaction structure, and involves a wide variety of market entities. According to our estimation, shadow banking in China amounted to RMB 51.1 trillion yuan at the end of 2017, which is 7.7 greater that at the end of 2008. Its average annual growth rate is 25.5%, the highest being 80%. At the end of 2018, shadow banking stocks were estimated at 48 trillion yuan. China's shadow banking has exerted broad influence on monetary policy regulation and financial stability. When expanding, it provides the real economy with additional financing other than traditional loans and bonds. When shrinking, it makes credit decrease faster, and increases the downward economic pressure. Against this background, it has important theoretical and timely practical significance for understanding the business structure of shadow banking and accurately estimating its aggregate quantity change, thus helping us to grasp the business of shadow banking, strengthen our understanding of the financial sector in economic development, and conduct high-quality theoretical modelling and empirical research. The main work and conclusions of this study are as follows. First, this paper gives a definition of shadow banking based on functionality and analyzes different shadow banking businesses by type. Shadow banking is defined as financial businesses that rely on the banking system and conduct banking business but without strict banking regulations. Specifically, it includes all financial businesses that are beyond the scope of on-balance sheet loans and bond investment of banks, the essential functionality being credit, maturity, and liquidity transformation, the essential result being the creation of “money,” and the principal entity being banks that endorse the development of shadow banking. Shadow banking in line with this definition is more closely correlated with monetary policy regulation and financial stability. This study provides a structural map of shadow banking focused on both the source and application of funding, and providing greater clarity compared with other maps in the current literature. Shadow banking business is classified into undiscounted bankers' acceptances, quasi-loans, and financial nesting. Quasi-loans include the repos of bankers' acceptances, inter-bank entrusted payments, and repos of beneficial rights to trust products. From the perspective of the funding source, financial nesting includes on-balance sheet bank funding and off-balance sheet wealth management business, while from the perspective of the application of funds, it includes trust products, wealth management products, entrusted loans, bond investments, etc. This study summarizes the transaction structure, business entity, business substance, source of funding, legal basis, and balance sheet expression of all business types. Second, this study accurately estimates the monthly data of shadow banking aggregates and balance sheet structure from 2002 till now. The seven principles for estimation are reliability, replicability, no double counting, conservative estimation, high frequency and continuity, comparability, and the matching of stock values with increments. We start from the liability side of the balance sheet, which fully covers all shadow banking businesses and produces its aggregate quantity, while avoiding double counting. The estimation results of this study are accurate. Further, after adequate statistical adjustment, the data reaches the goal of matching stock values and increments, making it convenient for calculating year-on-year and month-on-month indicators. This study presents detailed data sources and estimation methods for each major item in the shadow banking balance sheet, with the liability side including undiscounted bankers' acceptances (on both asset and liability sides), inter-bank quasi-loans, financial nesting (bank funding), and financial nesting (bank wealth management business), while the asset side includes entrusted loans, trust loans, bond investments, and other assets. The results are monthly time series data for each major item from 2002 until now.Estimation methods and data in this paper could be tested and improved by future researchers. The detailed and accurate data in this paper also lays solid foundation for future research on shadow banking.
[1]陈剑和张晓龙,2012,《影子银行对我国经济发展的影响——基于2000—2011年季度数据的实证分析》,《财经问题研究》第8期,第66~72页。 [2]封思贤、居维维和李斯嘉,2014,《中国影子银行对金融稳定性的影响》,《金融经济学研究》第29卷第4期,第3~12页。 [3]李泉、郭一凡和孟方方,2017,《影子银行会影响商业银行的稳定性吗?——来自中国2005-2015年103家商业银行的证据》,《湖南财政经济学院学报》第33卷第6期,第22~29页. [4]李向前、诸葛瑞英和黄盼盼,2013,《影子银行系统对我国货币政策和金融稳定的影响》, 《经济学动态》第5期,第81~87页。 [5]骆振心和冯科,2012,《影子银行与我国货币政策传导》,《武汉金融》第4期,第19~22页。 [6]毛泽盛和万亚兰,2012,《中国影子银行与银行体系稳定性阈值效应研究》,《国际金融研究》第11期,第65~73页。 [7]盛松成、徐诺金和张文红,2015,《社会融资规模理论与实践》,第二版.北京: 中国金融出版社。 [8]孙国峰和贾君怡,2015,《中国影子银行界定及其规模测算——基于信用货币创造的视角》,《中国社会科学》第11期,第92~110+207页。 [9]王浡力和李建军,2013,《中国影子银行的规模、风险评估与监管对策》,《中央财经大学学报》第5期,第20~25页。 [10]王振和曾辉,2014.《影子银行对货币政策影响的理论与实证分析》,《国际金融研究》第12期,第58~67页。 [11]温信祥和苏乃芳,2018,《大资管、影子银行与货币政策传导》,《金融研究》第10期,第38~54页。 [12]吴晓灵,2017,《最关注影子银行风险》,中国证券网。(2017-3-9) [13]张明,2013,《中国影子银行:界定、成因、风险与对策》,《国际经济评论》第3期,第82~92+6页。 [14]中央国债登记结算有限责任公司和全国银行业理财信息登记系统,2014~2018,中国银行业理财市场年度报告,北京:中央国债登记结算有限责任公司柜台市场部。 [15]Acharya, Viral V., Jun Qian, and Zhishu Yang. 2016. “In the Shadow of Banks: Wealth Management Products and Issuing Banks' Risk in China” , Working Paper. (2016~11-5) [16]Allen, Franklin, Yiming Qian, Guoqian Tu, and Frank Yu. 2018.“ Entrusted loans: A close Look at China's Shadow Banking System”. Journal of Financial Economics, forthcoming. [17]An, Ping, and Mengxuan Yu. 2018. “Neglected Part of Shadow Banking in China”.International Review of Economics & Finance, 57∶211~236. [18]Barth, James R., Tong Li, Wen Shi, and Pei Xu. 2015. “China's Shadow Banking Sector: Beneficial or Harmful to Economic Growth?”Journal of Financial Economic Policy, 7 (4):421~445. [19]Chen, Kaiji, Jue Ren, and Tao Zha. 2018a. “The Nexus of Monetary Policy and Shadow Banking in China”, American Economic Review, 108(12): 3891~3936. [20]Chen, Zhuo, Zhiguo He, and Chun Liu. 2018b. “The Financing of Local Government in China: Stimulus Loan Wanes and Shadow Banking Waxes”, Working Paper. (2018~10-12). [21]Ehlers, Torsten, Steven Kong, and Feng Zhu. 2018.“ Mapping Shadow banking in China: Structure and Dynamics”, BIS Working Papers, No.701. (2018-2-12) [22]Elliott, Douglas, Arthur Kroeber, and Yu Qiao. 2015. “Shadow Banking in China: A Primer ”,Research Paper, The Brookings Institution. (2015~3) [23]Financial Stability Board. 2011. “Shadow Banking: Strengthening Oversight and Regulation”, Basel, Switzerland: Bank for International Settlement. (2011-10-27) [24]Financial Stability Board. 2012-2017. “Global Shadow Banking Monitoring Report”, Basel, Switzerland: Bank for International Settlement. [25]International Monetary Fund. 2000. “Monetary and Financial Statistics Manual”, Washington, DC, USA. [26]Lu, Yunlin, Haifeng Guo, Erin H. Kao, and Hung-Gay Fung. 2015. “Shadow Banking and Firm Financing in China”, International Review of Economics & Finance, 36∶40~53. [27]Sheng, Andrew, and Ng Chow Soon. 2015. “Bringing Shadow Banking into the Light: Opportunity for Financial Reform in China”, Fung Global Institute Asian Perspectives Global Issues (FGI report). (2015~3) [28]Zhu, Xiaodong. 2018.” The Varying Shadow of China's Banking System”,University of Toronto Working Paper No.605.