基于GARCH模型的A股与香港股市行业板块波动性分析
Volatility Analysis of Industry Sector in A-share and Hong Kong Stock Market Based on GARCH Model
摘要: 选取2013年6月3日至2018年6月3日间中国大陆A股中证300行业指数和香港股市恒生行业指数,利用GARCH族模型对大陆A股和香港股市不同行业板块的日收益率序列进行波动性建模分析,结果表明:大陆A股和香港股市各行业板块收益率的波动均存在较强的聚集性与持续性,且波动的衰减较慢,而A股市场的波动持续性强于港市;香港股市各行业板块对当日信息的敏感度整体水平比大陆股市高,但具体行业反应存在较大的差异性;大陆A股与香港股市相同行业板块间的联动性不强,同步联动的行业板块明显少于异动性板块;大陆A股与香港股市的部分板块存在非对称性,并且,大陆股市的行业板块受好消息的影响程度更大,而香港股市的行业板块受坏消息的影响程度更大。投资者可以依据A股与香港股市不同行业板块波动性的特征配置其投资组合。
Abstract: This paper uses GARCH family model to analyze the volatility of daily return series of different sec-tors in mainland A-share market and Hong Kong stock market by selecting CSI 300 industry index and Hang Seng industry index from June 3, 2013 to June 3, 2018. The results show that the volatility of the returns of different sectors in mainland A-share and Hong Kong stock markets has strong aggregation and persistence, and the decline of volatility is slow, while the volatility of A-share market is more persistent than that of Hong Kong stock market. Hong Kong stock market is more sensitive than the mainland stock market to the information of the day, but there are great differ-ences in specific industry reactions. The linkage between Mainland A-share and Hong Kong stock market is not strong, and the synchronous linkage between the same industry sectors is signifi-cantly less than that of the heterogeneous sectors. There is asymmetry to some sectors in mainland A-share market and Hong Kong stock market, and the industry sectors of mainland A-share market are more greatly affected by good news, while the industry sectors of Hong Kong stock market are more greatly affected by bad news. Investors should allocate their portfolios according to the vola-tility characteristics of different sectors in A-share and Hong Kong stock markets.
文章引用:何菊香, 吴宇晨. 基于GARCH模型的A股与香港股市行业板块波动性分析[J]. 现代管理, 2019, 9(5): 677-690. https://doi.org/10.12677/MM.2019.95083

参考文献

[1] Fama, E.F. (1970) Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25, 383-417. [Google Scholar] [CrossRef
[2] Poterba, J.M. (1990) Transmission of Volatility between Stock Markets: Discussion. Review of Financial Studies, 3, 5-33. [Google Scholar] [CrossRef
[3] Awartani, B.M.A. and Corradi, V. (2005) Predicting the Volatility of the S & P-500 Stock Index via GARCH Models: The Role of Asymmetries. International Journal of Forecasting, 21, 167-183. [Google Scholar] [CrossRef
[4] Morrison, W.M. (2016) China’s Recent Stock Market Volatility: What Are the Implications?
[5] Ngene, G., Tah, K.A. and Darrat, A.F. (2017) Long Memory or Structural Breaks: Some Evidence for African Stock Markets. Review of Financial Economics, 34, 61-73. [Google Scholar] [CrossRef
[6] 陈千里, 周少甫. 上证指数收益的波动性研究[J]. 数量经济技术研究, 2002, 19(6): 122-125.
[7] 宋亚琼, 王新军. 基于动态估计误差的中国股市波动率建模与预测[J]. 中国管理科学, 2017, 25(9): 19-27.
[8] 鲁旭, 赵迎迎. 沪深港股市动态联动性研究——基于三元VAR-GJR-GARCH-DCC的新证据[J]. 经济评论, 2012(1): 97-107.
[9] 丁振辉, 徐瑾. 上海和香港两地股市联动性研究——基于GARCH模型的分析[J]. 金融发展研究, 2013(5): 20-25.
[10] 陈九生, 周孝华. 沪港通背景下沪港股市联动性研究[J]. 北京理工大学学报(社会科学版), 2017, 19(2): 87-93.
[11] 龚朴, 李梦玄. 沪港股市的波动溢出和时变相关性研究[J]. 管理学报, 2008, 5(1): 96-100.
[12] 杨瑞杰, 张向丽. 沪港通对大陆、香港股票市场波动溢出的影响研究——基于沪深300指数、恒生指数高频数据[J]. 金融经济学研究, 2015, 30(6): 49-59.
[13] 李红权, 何敏园. 我国股市的对外溢出效应与国际影响力研究——基于Copula-DCC-GARCH模型[J]. 系统科学与数学, 2017, 37(8): 1790-1806.
[14] Engle, R.F. (1982) Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of UK Inflation. Econometrica, 50, 987-1008. [Google Scholar] [CrossRef
[15] 曹伟龙. 应用ARCH模型对中国股市波动性的实证分析[J]. 世界经济情况, 2006(1): 19-22.
[16] 张玉春. 中国股市收益的ARCH模型与实证分析[J]. 首都经济贸易大学学报, 2006, 8(1): 85-88.
[17] 王敏, 张萍. 初探我国沪市股价波动性——基于ARCH模型和GARCH模型[J]. 科技创业月刊, 2010(1): 265-266.
[18] 刘湖, 王莹. 股票市场波动性研究-基于ARMA-TGARCH-M模型的实证分析[J]. 北京航空航天大学学报(社会科学版), 2017, 30(4): 56-66.
[19] 谭璇. 基于GARCH模型族的中国股市波动率检测[J]. 武陵学刊, 2018, 43(6): 31-37.
[20] Schwartz, R.A. and Altman, E.I. (1973) Volatility Behavior of Industrial Stock Price Indices. Journal of Finance, 28, 957-971. [Google Scholar] [CrossRef
[21] Chen, M.W. and Zhu, J. (2007) Volatility Clustering within Industries: An Empirical Investigation. American Journal of Business, 22, 33-44. [Google Scholar] [CrossRef
[22] 劳兰珺, 邵玉敏. 行业股票价格指数波动特征的实证研究[J]. 南开管理评论, 2005, 8(5): 4-8.
[23] Hamao, Y. and Masulis, R.W. (1990) Correlations in Price Changes and Volatility across International Stock Markets. Review of Financial Studies, 3, 281-307. [Google Scholar] [CrossRef