Economic policy uncertainty and industry risk on China’s stock market |
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| Institution: | 1. School of Management Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China;2. China Institute of Manufacturing Development, Nanjing University of Information Science & Technology, Nanjing 210044, China;3. School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, Zhejiang 310018, China;4. Collaborative Innovation Center for Statistical Data Engineering Technology and Application, Zhejiang Gongshang University, Hangzhou 310018, China;1. School of Economics, Nankai University, Tianjin 300071, China;2. Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200030, China;3. School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing 100049, China;1. AVIC Industry-Finance Holdings Co., LTD, Beijing 600705, China;2. Institute of Finance, Jinan University, Guangzhou 510632, China;1. Faculty of Economics and Administrative Sciences, Yarmouk University, Irbid 21163, Jordan;2. School of Management and Logistic Sciences, German Jordanian University, Amman 11180, Jordan;3. Shaheed Zulfikar Ali Bhutto Institute of Science and Technology (SZABIST), Islamabad, Pakistan;4. South Ural State University, 76, Lenin Prospekt, Chelyabinsk, Russian Federation;5. Department of Accounting and Finance, University of Stirling, Stirling, UK;6. Faculty of Economics and Administrative Sciences, Yarmouk University, Irbid 21163, Jordan |
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| Abstract: | We propose a dynamic mixture Copula with time-varying weight, which is endowed with generalized autoregressive score dynamics. Based on this model, we portray the lower-tail dependence between the return of WIND first-level industry and CSI-300 index as a proxy variable for the industry risk in China’s stock market, and use the VAR-GARCH-in-mean model based on BEKK-GARCH to deconstruct the different impact of the economic policy uncertainty (EPU) on industry risk of the first and second moments in terms of four policy categories, namely fiscal policy, monetary policy, trade policy, and foreign exchange rate and capital account policy. The results are followed. Firstly, the risk of Consumer Discretionary is averagely the highest, while the risk of Utilities remains the lowest. Secondly, category-specific EPU has no significant mean spillover to the risk of overall industries, while the variance spillover is significant for all the cases. Thirdly, except for Real Estate, the GARCH-in-mean effect is not significant of EPU on industry risks. Further more, all those three kinds of impact show industrial heterogeneities. To avoid systemic risks, we advise that the issue of economic policy should be forward-looking, consistent, and targeted, especially for sensitive industries. |
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| Keywords: | Economic policy uncertainty Industry risk Spillover effects Copula GARCH-in-mean |
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