互联网金融对商业银行风险承担的影响:基于中国商业银行的分析外文翻译资料

 2022-03-29 09:03

附录B外文原文

The impact of Internet finance on commercial banksrsquo; risk taking: evidence from China

Pin Guo and YueShen

Abstract

Background: Internet finance has grown rapidly in China over the past years.Through introducing internet financersquo;s “reducing management cost” and “raisingcapital cost” effects into bank risk-taking model, this paper systematically investigatesthe dynamic and heterogeneous influence of internet finance on China commercialbanksrsquo; risk-taking.

Methods: Using internet finance index based on “text mining” and data of 36commercial banks from 2003 to 2013, we makes an SYS-GMM test.

Results: The results show, firstly, the impact of internet finance on China commercialbanksrsquo; risk-taking is a “U” trend. The initial development of internet finance can helpcommercial banks to reduce management cost and risk-taking, but then internetfinance will raise capital cost, and turn to exacerbate banksrsquo; risk-taking. Secondly, theresponse of China commercial banksrsquo; risk-taking is heterogeneous. Large commercialbanksrsquo; response is slow, while small and medium banksrsquo; response is relatively sensitive.

Conclusions: The conclusion showed the complexityof the implication mechanism andfunctional process of how internet finance affected Chinacommercial banksrsquo; risk-taking.

Keywords: Internet finance, Commercial bank risk taking, System GMM estimation

JEL classification: E44, G21, G29

Background

From simple imitation a decade ago to todayrsquo;s proactive innovation, Chinarsquo;s Internet finance has demonstrated its role as the driving force,which should not beneglected. Over thepast 2 years, in particular, Internet finance has emerged as the “blueocean” enjoying tremendous development where numerous virtual and real enterprises vied to swim into that area. On July 18, 2015, the Guideline on Promoting the Healthy Development of Internet Finance (hereinafter referred to as the Guideline) was jointly issued by 10 ministries and departments including the Peoplersquo;s Bank of China. The Guideline indicated that the deep integration of Internet and finance was a prevailing trend, which would exert a profound influence on financial organization, financial products and financial service, but Internet finance had not changed the features of financial risks such as secret, contagious, wide-spread, and sudden. As a new type of finance business, Internet finance has broken the traditional boundary, propelled the processof financial disintermediation, and brought an overall impactcommercial banking. Against this background, the ever increasing importance has been attached to the discussion on the relation between Internet finance and commercialbanks.

Based on the documents we grasped, scholars differed in their opinions toward the definition of Internet finance. Shahrokhi (2008) maintained that Internet finance was a third financing model which was different from either the indirect financing of commercial bank or the direct financing in capital market. Instead, Zhiwu(2014) pointed out that Internet finance was an extension and upgrading of traditional financial service and was regulatory arbitrage taking advantage of the defect of Chinarsquo; financial system, instead of the so-called new finance. But the unveiling of this Guideline ended the de- bate. It clearly showed that Internet finance was a new model of financing sector where traditional financial institutions and Internet corporations conducted accommodation of funds, payment, investment, and information intermediary service by way of Internet technology and information communication technology.

The integration relation between Internet finance and traditional finance has been highly focused. From the perspective of financial function, Syed and Nida(2013) considered that Internet finance and commercial bank enjoyed their advantages respectively regarding financial functions, and both of them should be guided to compete and cooperate so as to promote the development and innovation of finance. From the perspective of financial repression, Guoqiang and Pengfei (2014) stressed that Internet finance was produced by interest rate control, and its rapid development would facilitate the process of the marketization of interest rate and raise the financing cost, which added the risk facing commercial banks. From the perspective of financial service, Zhilai(2015) indicated that Internet finance exerted great impact on commercial bank including its debt, asset, and intermediary service and would create an atmosphere with commercial bank featuring rapid diversion of debt and dislocation competition among assets, as well as rivalry against Internet finance in payment. In addition, viewed from the perspective of technology spillover, Yue and Pin (2015) analyzed how the promotion mechanism caused by Internet finance function on total factor productivity of commercial bank.

It has been another focus of spill-over effect and risk management targeting Internet finance. Xiaoqiu (2015) pointed out that Internet finance reduced transaction cost, facilitated market competition, enriched the finance implication, and pushed forward the financial reform. However, some risks also emerged rangingfrom dependence on technology and absence of trust to failure in business due to the lack of rules and regulations. Thus, itis imperative to conduct supervision and management over Internet finance and adhere to the principles of “lawful, moderate, classified, coordinated and innovative supervision and management,” so as to promote its sustainable development (Haier and Wuguang

附录A 译文

Pin Guo and Yue Shen

背景:在中国过去一年中,互联网金融发展迅速。本文通过引入互联网金融的“降低管理成本”和“提高资本成本”影响到银行的风险模型,系统地研究了动态、异构的网络金融对中国商业银行的风险承担影响。

结果:结果表明,首先,互联网金融对中国商业银行风险承担的影响是一个“U”的趋势。互联网金融的初步发展可以帮助商业银行降低管理成本和风险承担,但互联网金融将提高资本成本,进而加剧银行的风险承担。其次,对中国商业银行风险的反应是不同的。大型商业银行的反应迟缓,中小银行的反应相对敏感。

关键词 互联网金融;商业银行风险承担;GMM检验

背景

基于我们掌握的文献,学者们对互联网金融的定义有不同看法。SaHROKHI(2008)认为,互联网金融是一种不同于商业银行间接融资或资本市场直接融资的第三种融资模式。相反,Zhiwu(2014)指出,互联网金融是传统金融服务的扩展和升级,只是利用对中国金融体系的缺陷,而不是所谓的金融创新。但这一指导方针的公布宣告了争论的结束。我们可以清楚地看到,互联网金融是依托传统金融机构和互联网公司,通过互联网技术和信息通信技术进行资金支付、投资和信息中介服务的新型融资模式。

互联网金融的溢出效果与风险管理成为人们关注的另一个焦点。Xiaoqiu(2015)指出,互联网金融降低了交易成本,促进了市场竞争,丰富了金融内涵,推动了金融改革。然而,由于缺乏规章制度,企业出现了一些诸如由于技术依赖和信任缺失导致失败的风险因素。因此,对互联网金融进行监督管理势在必行,就像(Haier、Wuguang 2015)指出的,坚持“合法、适度、分类、协调、创新的监督管理”原则,促进其可持续发展。

(1)从微观的角度,阐述了互联网金融对商业银行风险承担的作用。现有的论文更多关注的是互联网金融对商业银行业务和功能的影响,缺乏对互联网金融与商业银行风险承担关系的探讨。在这篇文章中,作者试图通过建立包含互联网金融约束的理论模型,阐述了网络金融对商业银行风险承担的动态和异质性影响,以拓宽对互联网金融的研究视角和改进方向来提供答案,以期拓宽对互联网金融的研究视角,完善商业银行风险承担的理论框架。

(3)从金融职能的角度,建立了衡量中国互联网金融发展水平的量化指标。 尽管中国的一些学者已经选择了一些数据来表示互联网金融的范围,但这些代理变量仅仅包括互联网的支付功能,而忽略了建设渠道,分配资源和管理风险等其他功能。笔者试图以“文本挖掘”技术为基础,在金融功能思想的基础上弥补本文的不足,从而建立互联网金融指标,使我们能够客观衡量互联网金融发展水平。

理论模型

在传统金融服务的互联网推广阶段,互联网金融的发展有利于提高商业银行的技术水平,给服务方式带来便利,降低管理成本。以在线服务为例,该平台的构建有助于商业银行突破时空限制,拓展客户链,提升业务流程,促进数据处理,降低服务成本,实现信息化和集约化管理。效率和程序。在第三方支付的繁荣时期,互联网金融不仅取代了商业银行的支付业务,更重要的是,“提取”了银行的活期存款。以Alipay为例,这种支付模式通过灵活的方法满足社会的各种支付要求,并通过其有利的电子商务平台将流动资金分配到自己的超额准备金账户中,从而降低了活期存款比率并间接增加了。银行筹集资金的成本。在互联网信贷和货币管理兴起的时期,互联网金融将加速价格竞争,推进利率市场化,提高商业银行资本成本(Guoqiang和Pengfei 2014)。以余额宝为例,虽然这种资金管理工具未能对传统金融规模产生巨大影响,但其实施的“鲶鱼效应”已经深刻地塑造了资本定价机制和资本供求模型,都打破了中国银行享有的高利润率的收入格局,有效地推动了价格市场化进程。

同时,盈利水平,市场竞争和风险承担之间的关系受到银行规模、经营策略、监管要求等多种因素的影响(Ariss 2010; Jimenez 2013)。中国的大中小型商业银行在产权结构,目标客户和政策限制等方面差异很大。因此,我们认为,不同的商业银行将通过互联网金融带来的冲击的反应是异质的。在以上分析的基础上,本文阐述了互联网金融对商业银行风险承担的影响。并以此为基础建立数学模型,探讨双方的相互关系。

结果与讨论

估计动态面板模型的前提条件是数据的稳定性。为此实施了Hadri测试,LLC测试和银行级IPS测试。表3显示,商业银行风险承担和流动性水平的变量均为1%的显着水平的稳定系列,排除了“假回归”模型的可能性。

表4报告了命题1的回归结果。模型(1)和(2)是以RISKA / E作为银行风险承担代理变量的基准回归结果。根据表4,在这两个模型中,因变量的第一个和第二个滞后项的回归系数显着为正,表明风险承担行为确实具有可持续性。

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表3 变量单位根检验

Variables

Hadri test

Statistics (figure P)

LLC test

Statistics (figure P)

IPS test

Statistics (figure P)

RISKA/E

2.327*** (0.010)

minus;8.883*** (0.000)

minus;4.229*** (0.000)

RISKLL

7.787*** (0.000)

minus;30.396*** (0.000)

minus;7.206*** (0.000)

RISKL/A

8.636*** (0.000)

minus;7.139*** (0.000)

minus;3.522*** (0.000)

LI

8.000*** (0.000)

minus;6.152*** (0.000)

minus;1.764** (0.039)

注:三种单位根检验的原始假设是不存在单位根变量;**和**分别代表1和5%水平的显著性。

表4 互联网金融对商业银行风险承担的动态影响

Variable

Model (1)

Model (2)

Model (3)

Model (4)

Model (5)

Model (6)

L1.RISK

0.2164***

0.2101***

0.7195***

0.8478***

0.5135***

0.4362***

(0.0025)

(0.

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