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

被引:10
作者
Chen, Qiang [1 ]
Gong, Yuting [2 ]
机构
[1] Shanghai Univ Finance & Econ, Sch Econ, Shanghai, Peoples R China
[2] Shanghai Univ, SHU UTS SILC Business Sch, Dept Econ & Finance, Room 511,Wenhui Bldg,20 Chengzhong Rd, Shanghai 201800, Peoples R China
基金
中国国家自然科学基金;
关键词
CSI; 300; index; futures; GARCH; Mixed-data sampling; Stock market crisis; TRADING VOLUME; GARCH MODEL; RETURN; HETEROSKEDASTICITY; DETERMINANTS; PRICES; INDEX;
D O I
10.1016/j.iref.2019.05.017
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
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.
引用
收藏
页码:102 / 121
页数:20
相关论文
共 43 条
[1]   Forecasting Value-at-Risk using block structure multivariate stochastic volatility models [J].
Asai, Manabu ;
Caporin, Massimiliano ;
McAleer, Michael .
INTERNATIONAL REVIEW OF ECONOMICS & FINANCE, 2015, 40 :40-50
[2]   Macro-Finance Determinants of the Long-Run Stock-Bond Correlation: The DCC-MIDAS Specification [J].
Asgharian, Hossein ;
Christiansen, Charlotte ;
Hou, Ai Jun .
JOURNAL OF FINANCIAL ECONOMETRICS, 2016, 14 (03) :617-642
[3]   The Importance of the Macroeconomic Variables in Forecasting Stock Return Variance: A GARCH-MIDAS Approach [J].
Asgharian, Hossein ;
Hou, Ai Jun ;
Javed, Farrukh .
JOURNAL OF FORECASTING, 2013, 32 (07) :600-612
[4]   THE INDEX OF LEADING INDICATORS - MEASUREMENT WITHOUT THEORY, 35 YEARS LATER [J].
AUERBACH, AJ .
REVIEW OF ECONOMICS AND STATISTICS, 1982, 64 (04) :589-595
[5]   Measuring Economic Policy Uncertainty [J].
Baker, Scott R. ;
Bloom, Nicholas ;
Davis, Steven J. .
QUARTERLY JOURNAL OF ECONOMICS, 2016, 131 (04) :1593-1636
[6]   Breaks and persistency: macroeconomic causes of stock market volatility [J].
Beltratti, A ;
Morana, C .
JOURNAL OF ECONOMETRICS, 2006, 131 (1-2) :151-177
[7]   The effect of index futures trading on volatility: Three markets for Chinese stocks [J].
Bohl, Martin T. ;
Diesteldorf, Jeanne ;
Siklos, Pierre L. .
CHINA ECONOMIC REVIEW, 2015, 34 :207-224
[8]   Does price efficiency increase with trading volume? Evidence of nonlinearity and power laws in ETFs [J].
Caginalp, Gunduz ;
DeSantis, Mark .
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2017, 467 :436-452
[9]  
Chen G., 2001, FINANC REV, V38, P153, DOI DOI 10.1111/J.1540-6288.2001.TB00024.X
[10]   A component model for dynamic correlations [J].
Colacito, Riccardo ;
Engle, Robert F. ;
Ghysels, Eric .
JOURNAL OF ECONOMETRICS, 2011, 164 (01) :45-59