The reaction of Chinese stock market to economic uncertainty has always been an important issue for practitioners and researchers. In theory, stock market tends to be more volatile when economic environment is unstable. With the rapid development of stock market in China, we are concerned about how uncertainty from economics impact stock market. The GARCH-MIDAS introduced by Engle et al. (2013) is employed to investigate whether information contained economic uncertainty can help to predict long-term components of the Chinese A-share and B-share variance. Economic uncertainty used in this paper includes macroeconomic uncertainty and economic policy uncertainty (EPU). Our sample consists of daily stock returns and monthly economic variables from January 2003 through December 2016. ARIMA models are used to remove the trends, leaving residuals as economic uncertainty variables before incorporated into MIDAS specification. The empirical analysis indicates that economic uncertainty would impact stock market volatility in a slight way, and difference exists between A-share and B-share. IP (Industrial Production) growth rate and inflation rate contribute to the volatility of A-share and B-share, IP is the most significant factor. Neither monetary policy nor Chinese EPU contributes to A-share volatility, while they are factors of B-share volatility. American EPU doesn't significantly drive Chinese stock market fluctuate. Furthermore, variance decomposition verifies our conclusion. MIDAS approach is an appropriate way to investigate long-term component and short-term component of stock market volatility, which helps to identify economic factors driving Chinese stock market volatility.
[1] Schwert G W. Why does stock market volatility change over time?[J]. The Journal of Finance, 1989, 44(5):1115-1153.
[2] Kim Y, Nelson C R. Pricing stock market volatility:Does it matter whether the volatility is related to the business cycle?[J]. Journal of Financial Econometrics, 2014, 12(2):307-328.
[3] 郑挺国,尚玉皇. 基于宏观基本面的股市波动度量与预测[J]. 世界经济,2014,(12):118-139.
[4] 陈其安,雷小燕. 货币政策、投资者情绪与中国股票市场波动性:理论与实证[J]. 中国管理科学,2017,25(11):1-11.
[5] Adrian T, Rosenberg J. Stock returns and volatility:Pricing the short-run and long-run components of market risk[J]. The Journal of Finance, 2008, 63(6):2997-3030.
[6] Baker S R, Bloom N, Davis S J. Measuring economic policy uncertainty[R].Working Paper, Stanford University, 2012.
[7] 田磊, 林建浩. 经济政策不确定性兼具产出效应和通胀效应吗?来自中国的经验证据[J]. 南开经济研究, 2016,(2):3-24.
[8] Bernanke B S, Gertler M, Gilchrist S. The financial accelerator and the flight to quality[R]. National Bureau of Economic Research, 1994.
[9] Rodrik D. Policy uncertainty and private investment in developing countries[J]. Journal of Development Economics, 1991, 36(2):229-242.
[10] Campbell J Y, Shiller R J. The dividend-price ratio and expectations of future dividends and discount factors[J]. Review of Financial Studies, 1988, 1(3):195-228.
[11] Barberis N, Huang Ming, Santos T. Prospect theory and asset prices[J]. The Quarterly Journal of Economics, 2001, 116(1):1-53.
[12] Bernanke B S. Irreversibility, uncertainty, and cyclical investment[J]. The Quarterly Journal of Economics, 1983, 98(1):85-106.
[13] Engle R F, Rangel J G. The spline-GARCH model for low-frequency volatility and its global macroeconomic causes[J]. Review of Financial Studies, 2008, 21(3):1187-1222.
[14] Beltratti A, Morana C. Breaks and persistency:Macroeconomic causes of stock market volatility[J]. Journal of Econometrics, 2006, 131(1):151-177.
[15] 董直庆, 王林辉. 我国通货膨胀和证券市场周期波动关系——基于小波变换频带分析方法的实证检验[J]. 中国工业经济, 2008,(11):35-44.
[16] Christiansen C, Schmeling M, Schrimpf A. A comprehensive look at financial volatility prediction by economic variables[J]. Journal of Applied Econometrics, 2012, 27(6):956-977.
[17] Paye B S. ‘Déjà vol’:Predictive regressions for aggregate stock market volatility using macroeconomic variables[J]. Journal of Financial Economics, 2012, 106(3):527-546.
[18] Asgharian H, Hou Aijun, Javed F. The importance of the macroeconomic variables in forecasting stock return variance:A GARCH-MIDAS approach[J]. Journal of Forecasting, 2013, 32(7):600-612.
[19] Bradley D, Pantzalis C, Yuan Xiaojing. Policy risk, corporate political strategies, and the cost of debt[J]. Journal of Corporate Finance, 2016, 40:254-275.
[20] Bordo M D, Duca J V, Koch C. Economic policy uncertainty and the credit channel:Aggregate and bank level US evidence over several decades[J]. Journal of Financial Stability, 2016, 26:90-106.
[21] Li Xiaolin, Balcilar M, Gupta R, et al. The causal relationship between economic policy uncertainty and stock returns in China and India:evidence from a bootstrap rolling window approach[J]. Emerging Markets Finance and Trade, 2016, 52(3):674-689.
[22] 陈国进, 张润泽, 姚莲莲. 政策不确定性与股票市场波动溢出效应[J].金融经济学研究, 2014, 29(5):70-78.
[23] Tsai I C. The source of global stock market risk:A viewpoint of economic policy uncertainty[J]. Economic Modelling, 2017, 60:122-131.
[24] 林建浩, 李幸, 李欢. 中国经济政策不确定性与资产定价关系实证研究[J]. 中国管理科学, 2014, 22(S1):222-226.
[25] 王明涛, 路磊, 宋锴. 政策因素对股票市场波动的非对称性影响[J]. 管理科学学报, 2012, 15(12):40-57.
[26] Engle R F, Ghysels E, Sohn B. Stock market volatility and macroeconomic fundamentals[J]. Review of Economics and Statistics, 2013, 95(3):776-797.
[27] Ghysels E, Sinko A, Valkanov R. MIDAS regressions:Further results and new directions[J]. Econometric Reviews, 2007, 26(1):53-90.
[28] 聂富强, 宋国军. 沪、深股市波动不对称性的实证分析[J]. 数理统计与管理, 2007, 26(1):172-177.
[29] 严武, 许荣, 史清华,等. 产权保护和市场信息不对称:来自中国A-B股的证据[J]. 经济研究, 2012(11):128-141.
[30] Andersen T G, Bollerslev T, Diebold F X,et al. Real-time price discovery in global stock, bond and foreign exchange markets[J]. Journal of International Economics, 2007, 73(2):251-277.
[31] 龚玉婷, 陈强, 郑旭. 谁真正影响了股票和债券市场的相关性?——基于混频Copula模型的视角[J]. 经济学:季刊, 2016,(3):1205-1224.
[32] Ding Zhuanxin, Granger C W J. Modeling volatility persistence of speculative returns:A new approach[J]. Journal of Econometrics, 1996, 73(1):185-215.
