供应链金融模式下的信用风险评价(8)

的收盘价数据。

③ BEKK模型向量表达式是Engle和Kroner于1995年在综

合了Baba、Engle、Kraft和Kroner(1991未发表手稿)研究基础上提出的由四人名字的第一个字母命名的一类多元GARCH模型的表示形式。

作者简介 张信东，山西大学管理学院教授、博士，研究方向为财务管理和金融工程；赵芳，山西大学管理科学与工程硕士研究生，研究方向为财务管理和金融工程

(上接第98页）

[13] 古志辉. 战略扩张与融资选择, 管理科学学报, 2004, 7(1): 19-24.[14] 周好文, 李辉. 中小企业的关系型融资实证研究及理论释义,

南开管理评论, 2005, 8(1): 69-74.

[15] 林海, 王鑫. 以民间商会为信用中介的中小企业投融资模式研

究, 南开管理评论, 2007, 10(4): 86-91.

作者简介 熊熊，天津大学管理学院副教授、博士、硕士生导师，研究方向为金融工程与金融风险管理；马佳，天津大学管理学院硕士研究生；赵文杰，深圳发展银行高级经济师；王小琰，天津大学管理学院硕士研究生；张今，天津财经大学商学院

Spillover Effects between Shanghai, Shenzhen and Hong

Kong: A Comparative Analysis Based on “The Through Train of Hong Kong Stocks”

Zhang Xindong, Zhao Fang

School of Management, Shanxi University

Abstract Focusing on whether “the through train of Hong Kong stock” has a significant impact on return and volatility spillover re-lationships between the Hong Kong, Shanghai and Shenzhen stock markets, this paper applies the VAR and Multiple GARCH model to data from the three stock markets. The evidence shows that no return spillover effect existed between the Hong Kong stock market and the other two markets before the announcement of “the through train of Hong Kong stock”, while a one-way volatility spillover effect emerged after the announcement. Additionally, the results indicate a bi-directional volatility spillover effect between the Hong Kong and Shanghai stock markets, and a one-way effect between the Hong Kong and Shenzhen stock markets. This shows that the launch of “the through train of Hong Kong stock” had a significant impact on the linkage between the Hong Kong stock market and the Shanghai and Shenzhen stock markets (in terms of mean and volatility spillovers). We believe, therefore, in the economic environment of continuing turmoil in global capital markets, we should speed up the gradual transformation of mainland market policy from one emphasizing administrative policy to one promoting economy policy. Restoring investor confidence by making reasonable policy adjustments should be a priority, as should the prudent promotion of financial product innovation. This should help to lay a solid foundation for the smooth, coordinated, healthy, and sustainable development of the mainland and Hong Kong capital markets. The time for enacting “the through train of Hong Kong stock” has not yet arrived. Given the deepening of the globalization process and the ever-increasing degree of open-ness in mainland stock markets, more and more capital will flow to Hong Kong stocks and the linkages between the Shanghai, Shenzhen and Hong Kong stock markets will become increasingly close. When and how “the through train of Hong Kong stock” can be successfully implemented will continue to be a topic worthy of further exploration.Key Words Stock Market; Spillover Effect; VAR Model; Multi-variate GARCH Model

Credit Risk Analysis of Supply Chain Finance

Xiong Xiong1, Ma Jia1, Zhao Wenjie2, Wang Xiaoyan1, Zhang Jin3

1. School of Management, Tianjin University; 2. Shenzhen Development Bank; 3. Business School, Tianjin University of Finance and EconomicsAbstract Rather than rely on credit models that mainly focus on individual SMEs’ financial data, supply chain finance models have provided a new and creative perspective to identify and evaluate the risks of SMEs. This paper concentrates on credit risk evaluation in the supply chain finance model, as well as developing a credit risk evaluation system that emphasizes both overall and debt ratings. In addition, we use Principal Component Analysis and logistic re-gression to construct a credit risk evaluation model that may solve the limitations of traditional methods of expert evaluation and thus make the entire evaluation system more objective and scientific. As for Principal Component Analysis that is conducted with the help of SPSS, the paper aims to identify the critical indices to use in logistic regression, since logistic regression is very sensitive to the relevance of the variables. Furthermore, comparing the differences between compliance probability for SMEs in both supply chain finance models and traditional banking credit models, we notice that supply chain finance may mitigate the difficulties of SMEs’ financing and thus enhance the efficiency of SMEs’ funding processes. To some extent, supply chain finance also serves to motivate the construction of basic customer databases. This is the primary prerequisite for the establishment of a fair and efficient SME financing system. The modification and perfection of the current credit evaluation system will improve financing efficiency. Finally, this paper examines eco-nomic trends and their effect on supply chain finance during the de-sign of the supply chain finance model. According to the results of related empirical analysis, the supply chain finance model combined with a perfect credit evaluation system will create a win-win situa-tion for banks and SMEs. That is a crucial contribution to the SME financing research in China.

Key Words Supply Chain Finance; Credit Risk; Risk Analysis; Confidence Probability