Do Policy Factors Affect Stock Market Volatility in Long-term?——Analysis Based on GARCH-MIDAS Model

Abstract

Abstract: Based on GARCH-MIDAS model, this paper empirically tests the influence of policy intensity on the long-term component of stock market volatility. The conclusions are as follows: stock capital supply policy, market laws, and market regulation can reduce the long-term component of stock market volatility. The influence of the stock capital supply policy decreases steeply over time, while the influences of market laws and market regulation are relatively lasting. Tax policy adjustment promotes the long-term component of stock market volatility, and such an impact is lasting. The influences of stock supply policy and monetary policy adjustment are not significant. The out-of-sample prediction analysis shows that adding the policy variables to GARCH-MIDAS model can effectively improve the prediction accuracy of the model.

Key words: Policy Factors, Policy Classification, Stock Market Volatility, GARCH-MIDAS Model

摘要： 基于GARCH-MIDAS模型实证检验了政策强度对股市波动长期成分的影响。研究发现:股票资金供应政策、市场法律法规、市场监管能降低股市波动长期成分。股票资金供应政策的影响随时间推移下降较快,市场法律法规、市场监管对市场波动的影响较为持久。税收政策调整会增加股市波动长期成分,且其影响是持久的。股票供应政策和货币政策的调整对于股市波动长期成分影响不显著。样本外预测结果表明,GARCH-MIDAS模型中加入政策变量能有效提升模型预测精度。

关键词: 政策因素, 政策分类, 股市波动, GARCH-MIDAS模型

CLC Number:

F830

ZHONG Lixin,YAO Qian,WANG Congcong. Do Policy Factors Affect Stock Market Volatility in Long-term?——Analysis Based on GARCH-MIDAS Model[J]. , 2020, 36(6): 51-62.

钟立新,姚前,王聪聪. 政策因素会长期影响股市波动吗？——基于GARCH-MIDAS模型的分析[J]. 财经论丛, 2020, 36(6): 51-62.

References

[1] 王朝阳,王振霞.涨跌停、融资融券与股价波动率——基于AH股的比较研究[J]. 经济研究,2017,(4):151-163.
[2] 陈海强,范云菲.融资融券交易制度对中国股市波动率的影响——基于面板数据政策评估方法的分析[J]. 金融研究,2015,(6):159-171.
[3] 卢骏,杨季超.融资融券交易对市场价格发现的影响——基于中国创业板与中小板的研究[J]. 财经论丛,2015,(11):43-51.
[4] 胡忆文.融资融券对我国股市波动性影响的实证研究[J]. 财经论丛,2017,(4):55-60.
[5] 曹栋,张佳.基于GARCH-M模型的股指期货对股市波动影响的研究[J]. 中国管理科学,2017,(1):27-32.
[6] 杨晓兰,金雪军.我国股票市场熔断机制的磁力效应:基于自然实验的证据[J]. 金融研究,2017,(9):165-181.
[7] 汪天都,孙谦.传统监管措施能够限制金融市场的波动吗?[J]. 金融研究,2018,(9):177-191.
[8] Gu M., Kang W., Xu B. Limits of Arbitrage and Idiosyncratic Volatility: Evidence from China Stock Market[J]. Journal of Banking & Finance, 2018, 86: 240-258.
[9] Brumm J., Grill M., Kubler F., et al. Margin Regulation and Volatility[J]. Journal of Monetary Economics, 2015, 75: 54-68.
[10] Deb S. S., Kalev P. S., Marisetty V. B. Price Limits and Volatility[J]. Pacific-Basin Finance Journal, 2017, 45: 142-156.
[11] Ngassam C. Effect of Price Limits on Volatility and Stock Returns in Emerging Markets: Evidence from the Johannesburg Stock Exchange[J]. Journal of Comparative International Management, 2002, 5(1):16-20.
[12] Al-Rjoub S.A.M., Abutabenjeh S. Price Limits and Stock Returns Volatility in Jordanian Banks[J]. International Journal of Monetary Economics and Finance, 2009, (2): 144-165.
[13] 史永东,蒋贤锋.中国证券市场印花税调整的效应分析[J]. 世界经济,2003,(12):63-70.
[14] 陈其安,张媛,刘星.宏观经济环境、政府调控政策与股票市场波动性[J]. 经济学家,2010,(2):90-97.
[15] Burman L.E., Gale W.G., Gault S.,et al. Financial Transaction Taxes in Theory and Practice[J]. National Tax Journal,2016,69(1):171-216.
[16] 杨继平,冯毅俊.利率调整对我国股市不同状态波动性的影响[J]. 管理科学学报,2017,(2):63-73.
[17] 贾盾,孙溪,郭瑞.货币政策公告、政策不确定性及股票市场的预公告溢价效应——来自中国市场的证据[J]. 金融研究,2019,(7):76-93.
[18] Chen E. T., Clements A. S&P 500 Implied Volatility and Monetary Policy Announcements[J]. Finance Research Letters, 2007, (4):227-232.
[19] 王博,李力,郝大鹏.货币政策不确定性、违约风险与宏观经济波动[J]. 经济研究,2019,(3):119-134.
[20] Iryna K.,Matt R.S. Volatility in Equity Markets and Monetary Policy Rate Uncertainty[J]. Journal of Empirical Finance,2018,45:68-83.
[21] Husted L.,Rogers J.,Sun B. Monetary Policy Uncertainty[J]. Journal of Monetary Economics,2019, (7):32-47.
[22] Engle R. F., Ghysels E., Sohn B. Stock Market Volatility and Macroeconomic Fundamentals[J]. Review of Economics & Statistics, 2013, 95(3):776-797.
[23] Asgharian H., Hou A.J., 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.
[24] Conrad C., Custovic A., Ghysels E. Long- and Short-Term Cryptocurrency Volatility Components: A GARCH-MIDAS Analysis[R]. Journal of Risk and Financial Management, 2018, 11(2): 1-12.
[25] Girardin, E.,Joyeux R. Macro Fundamentals as a Source of Stock Market Volatility in China: A GARCH-MIDAS Approach[J]. Economic Modelling, 2013, 34: 59-68.
[26] 夏婷,闻岳春.经济不确定性是股市波动的因子吗？——基于GARCH-MIDAS模型的分析[J]. 中国管理科学,2018,(12):1-11.
[27] 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.
[28] Tsay R.S.金融时间序列分析[M]. 王远林,王辉,潘家柱,译.北京:人民邮电出版社,2012.
[29] Ghysels E. Macroeconomics and the Reality of Mixed Frequency Data[J]. Journal of Econometrics, 2016, 193(2):294-314.
[30] Cochrane J.H. Finance: Function Matters, Not Size[J]. Journal of Economic Perspectives, 2013, 27(2):29-50.
[31] 朱相平,彭田田.QFII持股对中国股票市场稳定性的影响——基于中美贸易摩擦背景下的研究[J]. 宏观经济研究,2019,(5):60-73.
[32] 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):118-136.
[33] Hansen P.R. A Test for Superior Predictive Ability[J]. Journal of Business & Economic Statistics,2005,23(4):365-380.