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Dynamic and Asymmetric Contagion Reactions of Financial Markets During the Last Subprime Crisis

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Abstract

To analyze the dynamic and asymmetric contagion reactions of financial markets during the last subprime crisis, this paper proposes a contagion reaction equation combined with the generalized auto regressive conditional heteroskedasticity process to develop a dynamic asymmetric contagion model, and then provides the Markov chain Monte Carlo estimation method of this new model. This paper then constructs an empirical study of two metals futures in China during the last subprime crisis period, applying the model to measure the impact of the contagion reactions as well as assess the model’s effectiveness. Our results show: (1) the financial contagion phenomenon is the reason why some financial markets experienced almost corresponding reactions during the subprime crisis; (2) financial contagion reactions behave conspicuously in three particular phases during the subprime crisis; (3) financial contagion reactions have predictive functions for financial market changes and can provide indicators for risk management during crisis periods.

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Notes

  1. See Table 1 for a full demonstration of this.

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Acknowledgments

The author would like to thank the editors and the anonymous reviewers for their insightful and constructive suggestions that have led to this improved version of the paper. This work was supported by the Natural Science Foundation of China (No. 71301141), China Postdoctoral Science Foundation (Nos. 2015M570792 and 2016T90864), Humanity and Social Science Youth Foundation of Ministry of Education of China (No. 13YJC630247), Applied Basic Research Programs of Science and Technology Commission of Yunnan Province (No. 2013FD029), Science Foundation and Major Project of Educational Committee of Yunnan Province (No. 2014Z100), and Philosophy and Social Science Foundation of Yunnan Province (No. YB2015087).

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Zhou, W. Dynamic and Asymmetric Contagion Reactions of Financial Markets During the Last Subprime Crisis. Comput Econ 50, 207–230 (2017). https://doi.org/10.1007/s10614-016-9606-z

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