The Frontiersin Economicand Management Research attempts to provide a plat form for the Chinese scholarsin mainland China to communicate with their peers over seasin economic and management research．The journal aims to publish articles that have conducted quality as well as innovative research， and that investigate major issues in economic and management research， and that address major economic and management issuesin the Chinese market．The journal encourage scross-fertilization of ideas among the fields of thinking and application of advanced analytical techniques in the research．It is also the journal?? sintention to suggest directions for future research， through the articles， to the Chinese scholars and to provide insights and readings for classroom use．The journal will make efforts to contribute to the development to feconomic and management research in mainland China．
目錄：
1 Transmission Mechanismof Stock Market Volatilitybetween Chinaandthe U. S.: Empirical Evidenceduring Subprime Crisis from EDCC-GARCH Model
Jinquan Liu Yueling Luo Guanglin Ji
10 Dimensionsof Consumer-Brand Bonds
Jing Huang Yang Tang Qinglan Lin
23 The Order Submission Behaviorssurrounding Open-Market Repurchase Announce Gments: The Examinationofa Missing Link Embeddedinthe Signaling Hypothesis Chaoshin Chiao Hsiang-Hsuan Chih Zi-May Wang Ya-Rou Hsu
61 Parameter Estimationin Hidden Markov Processwith Kalman Filter
Ping Tianand Yaozhong Hu
70 Entrepreneurs?? Mental Modelsand Strategic Choice
Guoqing Zouand Hui Gao
80 A Studyon Interpersonal Emotional Contagioninthe Service Industry
Cedric Hsi-Jui Wu Hung-Jen Li Pei-Ru Lin Hsiao-Chun Liao
95 The Economic Growth Modelbasedon Entrepreneurship
Xiuyan Zhangand Song Zhang
112 Structural Equation Modelingof Human Grelated Issuesin Cellular Manufacturing
Jian Gtong Zhang Ling Jin Lei Zeng Yan Gwen Dong
122 The Basisfor Determining Executive Compensationsin State-Owned Enterprises: Operating Performanceor Earnings Management
Yanqiu Zhang
內容試閱：
Transmission Mechanism of Stock Market Volatility
between China and the U.S. ： Empirical Evidence during Subprime Crisis from EDCC-GARCH Model
Jinquan Liu Yueling Luo Guanglin Ji
Center for Quantitative Economics，Jilin University
Abstract ： This paper studies the dynamic correlation between Chinese and the U.S. stock market prior and posterior to the 2007 Subprime Crisis. By incorporating timc-diffcrcncc in our empirical study，wc analyze the possible existing transmission mcchanism between these two markets by using EDCC- GARCH model and concludc that EDCC-GARCH model could well dcpict the relationship between Chinese and U.S. stock market. Furthermore?the performance of the U.S. market 1 -day ago would lead
Chinese market move to the same direction. Thc dynamic correlation cocfficicnts from 2005 to 2010 suggest that the relationship between Chinese and the U.S. stock markets bccomcs more stable with the developing of Chinese financial market.
Key Words ： financial markets; volatility; correlation analysis; EDCC-GRCH model
Introduction
Measuring the temporal and intertemporal relationships between different financial markets is a long-lasting research topic of risk management and portfolio construction. Since 2008，the Subprime Crisis has swept the whole world，and seriously affected the economic growth in China. As the negative impact of the crisis has gradually retreated from the financial markets around the world after 2010，lots of research have been conducted by both policy makers and researchers. However，there are lots of questions still need to be answered， such as， “How well can we depict the relationship between Chinese and the U.S. financial markets?” “How could the volatility of the U.S. financial market transmit to Chinese market?”
In order to analyze and measure the relationships between different financial markets，correlation analysis is one of the most important tools. Mostly? practitioners would use two methods-constant and dynamic conditional correlation coefficient. Most researchers view the latter as the better way for analyzing the real-time relationship between the two different series. However，in a large number of economic and financial literatures，ARCH model has become the standard research tool for volatility modeling， particularly on correlation of volatilities. In the past several years，both the univariate and the multivariate GARCH model MGARCH model①）have been thoroughly studied for relevant researches \n financial econometrics.
Since Bollerslev 1990 developed the constant conditional correlation GARCH model henceforth，the CCC-GARCH model， multivariate analysis has become an essential framework for understanding the relationship between the co volatilities of several economies and markets. Besides，this model well contained the tools mentioned previously. Engle 2002 extended the CCC-GARCH model to the dynamic conditional correlation， after which proposed the DCC-GARCH model. Furthermore，He and Terasvirta 2004 raised the extension of constant conditional correlation GARCH model henceforth， the ECCC-GARCH， and argued that this model would better exhibit the correlation structures of different financial series.
Most studies analyze and measure the correlation between Chinese financial markets by using ARCH-type model，especially DCC-GARCH model proposed by Engle 2002. For Example， Fan and Zhang 2003 analyzed the volatility of Shanghai and Shenzhen stock markets by using genetic algorithm and MGARCH model. Li and Zhang 2007 studied the spurious persistence in the correlation of Shanghai and Shenzhen stock markets by using multivariable structural change TGARCH model. Qin and Zheng 2008 employed ADCC model to predict the correlation of Chinese main stock indices.
However， the DCC-GARCH model is buiit on the assumption that non-diagonal matrix elements are zero， which excludes the “spillover effects” from the model. Until now? a large number of literature have found that the lag of conditional variance tend to affect the volatility of another variable in financial market. Thus，some biases would arise if we apply this model to the analysis in which spillover effect exists.
On the contrary， the EDCC-GARCH model considered in this paper circumvents the problems mentioned above. The reason why we extend the DCC-GARCH model is that this generalization allows us including the volatility spillover effect between Chinese and the U.S. stock markets. Based on this specification， EDCC-GARCH model can solve the problem on correlation and depict the volatility spillover effect between different financial markets by assuming nonzero of all the elements in the coefficient
matrix. Statistically speaking， the diagonal elements in the parameter matrix of MGARCH model reflect the auto correlation of every specific variance-covariance series，and non-diagonal elements reflect the correlations between different series. So this specification for the correlation coefficients not only con
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