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The economic sources of China's CSI 300 spot and futures volatilities before and after the 2015 stock market crisis

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  • Chen, Qiang
  • Gong, Yuting

Abstract

The 2015 Chinese stock market crisis has increased focus on the factors that determine the volatility of stock spot and futures markets. In this paper, we investigate the economic sources of CSI 300 spot and futures volatilities before and after the stock market crash based on the generalized autoregressive conditional heteroskedasticity model with the mixed frequency data sampling scheme (GARCH-MIDAS). It shows that the risks of the CSI 300 Index tend to increase with higher inflation, lower economic growth, tighter credit conditions and more variant credit policies, while the risks of CSI 300 futures tend to increase with higher inflation, tighter credit conditions, more variant inflation rates and more variant credit policies. The effects of economic fundamentals are greater and more prolonged than the effects of economic uncertainty and speculative trading. Investors are advised to pay attention to the changes in price levels, economic development and credit policies when managing their portfolio risks. More importantly, as speculation has contributed little to the risks of CSI 300 futures in the post-crisis period, regulators are advised to ease trading restrictions and resume index futures trading gradually.

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  • Chen, Qiang & Gong, Yuting, 2019. "The economic sources of China's CSI 300 spot and futures volatilities before and after the 2015 stock market crisis," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 102-121.
    • Handle: RePEc:eee:reveco:v:64:y:2019:i:c:p:102-121
    DOI: 10.1016/j.iref.2019.05.017
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    as
    1. Pierdzioch, Christian & Döpke, Jörg & Hartmann, Daniel, 2008. "Forecasting stock market volatility with macroeconomic variables in real time," Journal of Economics and Business, Elsevier, vol. 60(3), pages 256-276.
    2. Jian Yang & Zihui Yang & Yinggang Zhou, 2012. "Intraday price discovery and volatility transmission in stock index and stock index futures markets: Evidence from China," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 32(2), pages 99-121, February.
    3. Bohl, Martin T. & Diesteldorf, Jeanne & Siklos, Pierre L., 2015. "The effect of index futures trading on volatility: Three markets for Chinese stocks," China Economic Review, Elsevier, vol. 34(C), pages 207-224.
    4. Owain Ap Gwilym & David McMillan & Alan Speight, 1999. "The intraday relationship between volume and volatility in LIFFE futures markets," Applied Financial Economics, Taylor & Francis Journals, vol. 9(6), pages 593-604.
    5. Wang, Gang-Jin & Jiang, Zhi-Qiang & Lin, Min & Xie, Chi & Stanley, H. Eugene, 2018. "Interconnectedness and systemic risk of China's financial institutions," Emerging Markets Review, Elsevier, vol. 35(C), pages 1-18.
    6. Jianping Mei & Jose A. Scheinkman & Wei Xiong, 2009. "Speculative Trading and Stock Prices: Evidence from Chinese A-B Share Premia," Annals of Economics and Finance, Society for AEF, vol. 10(2), pages 225-255, November.
    7. Caginalp, Gunduz & DeSantis, Mark, 2017. "Does price efficiency increase with trading volume? Evidence of nonlinearity and power laws in ETFs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 436-452.
    8. Kao, Lie-Jane & Wu, Po-Cheng & Lee, Cheng-Few, 2012. "Time-changed GARCH versus the GARJI model for prediction of extreme news events: An empirical study," International Review of Economics & Finance, Elsevier, vol. 21(1), pages 115-129.
    9. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    10. Gao, Bin & Yang, Chunpeng, 2017. "Forecasting stock index futures returns with mixed-frequency sentiment," International Review of Economics & Finance, Elsevier, vol. 49(C), pages 69-83.
    11. Robert F. Engle & Eric Ghysels & Bumjean Sohn, 2013. "Stock Market Volatility and Macroeconomic Fundamentals," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 776-797, July.
    12. Asai, Manabu & Caporin, Massimiliano & McAleer, Michael, 2015. "Forecasting Value-at-Risk using block structure multivariate stochastic volatility models," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 40-50.
    13. Yang, Lu & Cai, Xiao Jing & Hamori, Shigeyuki, 2018. "What determines the long-term correlation between oil prices and exchange rates?," The North American Journal of Economics and Finance, Elsevier, vol. 44(C), pages 140-152.
    14. Hossein Asgharian & Charlotte Christiansen & Ai Jun Hou, 2016. "Macro-Finance Determinants of the Long-Run Stock–Bond Correlation: The DCC-MIDAS Specification," Journal of Financial Econometrics, Oxford University Press, vol. 14(3), pages 617-642.
    15. Christian Conrad & Karin Loch, 2015. "Anticipating Long‐Term Stock Market Volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(7), pages 1090-1114, November.
    16. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    17. Corradi, Valentina & Distaso, Walter & Mele, Antonio, 2013. "Macroeconomic determinants of stock volatility and volatility premiums," Journal of Monetary Economics, Elsevier, vol. 60(2), pages 203-220.
    18. Lamoureux, Christopher G & Lastrapes, William D, 1990. "Heteroskedasticity in Stock Return Data: Volume versus GARCH Effects," Journal of Finance, American Finance Association, vol. 45(1), pages 221-229, March.
    19. Hossein Asgharian & Ai Jun Hou & Farrukh Javed, 2013. "The Importance of the Macroeconomic Variables in Forecasting Stock Return Variance: A GARCH‐MIDAS Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(7), pages 600-612, November.
    20. Auerbach, Alan J, 1982. "The Index of Leading Indicators: "Measurement without Theory," Thirty-Five Years Later," The Review of Economics and Statistics, MIT Press, vol. 64(4), pages 589-595, November.
    21. Colacito, Riccardo & Engle, Robert F. & Ghysels, Eric, 2011. "A component model for dynamic correlations," Journal of Econometrics, Elsevier, vol. 164(1), pages 45-59, September.
    22. Yeh, Yin-Hua & Lee, Tsun-Siou, 2000. "The interaction and volatility asymmetry of unexpected returns in the greater China stock markets," Global Finance Journal, Elsevier, vol. 11(1-2), pages 129-149.
    23. Christos Floros & Enrique Salvador, 2016. "Volatility, trading volume and open interest in futures markets," International Journal of Managerial Finance, Emerald Group Publishing Limited, vol. 12(5), pages 629-653, October.
    24. Chen, Gong-meng & Firth, Michael & Rui, Oliver M, 2001. "The Dynamic Relation between Stock Returns, Trading Volume, and Volatility," The Financial Review, Eastern Finance Association, vol. 36(3), pages 153-173, August.
    25. Girardin, Eric & Liu, Zhenya, 2005. "Bank credit and seasonal anomalies in China's stock markets," China Economic Review, Elsevier, vol. 16(4), pages 465-483.
    26. Kleidon, Allan W & Whaley, Robert E, 1992. "One Market? Stocks, Futures, and Options during October 1987," Journal of Finance, American Finance Association, vol. 47(3), pages 851-877, July.
    27. Francis X. Diebold & Kamil Yılmaz, 2007. "Macroeconomic Volatility and Stock Market Volatility,World-Wide," Koç University-TUSIAD Economic Research Forum Working Papers 0711, Koc University-TUSIAD Economic Research Forum.
    28. Wang, Gang-Jin & Xie, Chi & Jiang, Zhi-Qiang & Stanley, H. Eugene, 2016. "Extreme risk spillover effects in world gold markets and the global financial crisis," International Review of Economics & Finance, Elsevier, vol. 46(C), pages 55-77.
    29. Robert F. Engle & Jose Gonzalo Rangel, 2008. "The Spline-GARCH Model for Low-Frequency Volatility and Its Global Macroeconomic Causes," The Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1187-1222, May.
    30. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
    31. Paye, Bradley S., 2012. "‘Déjà vol’: Predictive regressions for aggregate stock market volatility using macroeconomic variables," Journal of Financial Economics, Elsevier, vol. 106(3), pages 527-546.
    32. Hou, Ai Jun, 2013. "Asymmetry effects of shocks in Chinese stock markets volatility: A generalized additive nonparametric approach," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 23(C), pages 12-32.
    33. Girardin, Eric & Joyeux, Roselyne, 2013. "Macro fundamentals as a source of stock market volatility in China: A GARCH-MIDAS approach," Economic Modelling, Elsevier, vol. 34(C), pages 59-68.
    34. Engle, Robert & Colacito, Riccardo, 2006. "Testing and Valuing Dynamic Correlations for Asset Allocation," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 238-253, April.
    35. Beltratti, A. & Morana, C., 2006. "Breaks and persistency: macroeconomic causes of stock market volatility," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 151-177.
    36. Ghysels, Eric & Seon, Junghoon, 2005. "The Asian financial crisis: The role of derivative securities trading and foreign investors in Korea," Journal of International Money and Finance, Elsevier, vol. 24(4), pages 607-630, June.
    37. Morelli, David, 2002. "The relationship between conditional stock market volatility and conditional macroeconomic volatility: Empirical evidence based on UK data," International Review of Financial Analysis, Elsevier, vol. 11(1), pages 101-110.
    38. Qian Han & Jufang Liang, 2017. "Index Futures Trading Restrictions and Spot Market Quality: Evidence from the Recent Chinese Stock Market Crash," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 37(4), pages 411-428, April.
    39. Ali F. Darrat & Shafiqur Rahman, 1995. "Has futures trading activity caused stock price volatility?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 15(5), pages 537-557, August.
    40. Gallant, A Ronald & Rossi, Peter E & Tauchen, George, 1992. "Stock Prices and Volume," The Review of Financial Studies, Society for Financial Studies, vol. 5(2), pages 199-242.
    41. Hou, Aijun & Suardi, Sandy, 2012. "A nonparametric GARCH model of crude oil price return volatility," Energy Economics, Elsevier, vol. 34(2), pages 618-626.
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