中国沪深股市结构性波动的政策性影响因素

中国管理科学 ›› 2012, Vol. 20 ›› Issue (6): 43-51.

中国沪深股市结构性波动的政策性影响因素

杨继平¹, 陈晓暄², 张春会¹

1. 北京航空航天大学经济管理学院,北京 100191;
2. 国网电力科学研究院,江苏南京 210003

收稿日期:2011-08-31 修回日期:2012-06-17 出版日期:2012-12-29 发布日期:2012-12-28
基金资助:
国家自然科学基金资助项目(70871003 71271011)

Policy Impact Factors on the Volatility of Shanghai and Shenzhen Stock Market in China

YANG Ji-ping¹, CHEN Xiao-xuan², ZHANG Chun-hui¹

1. School of Economics and Management, Beihang University, Beijing 100191, China;
2. State Grid Electric Power Research Institute,Nanjing 210003,China

Received:2011-08-31 Revised:2012-06-17 Online:2012-12-29 Published:2012-12-28

摘要/Abstract

摘要： 本文利用划分均值和方差变点的迭代累积平方和算法(ICSS:MV)对上证综指和深证成指1996年12月16日至2010年12月31日的日收益率序列进行结构变点的检验,通过将结构变点与重大事件对应选取影响沪深股市结构性波动的政策性事件,并根据选取的事件将样本区间分成13个子区间。为了避免参数模型中模型误设的缺陷,利用非参数GARCH模型估计样本区间的波动率;最后利用N-W核回归估计对非参数GARCH估计的波动率与收益率进行回归,分析股市结构性波动产生的政策性影响因素。通过分析发现央行调整存贷款基准利率和存款准备金率、国有股的减持、允许保险公司等机构投资者买卖证券投资基金、调整印花税等政策性因素是造成我国股市变结构波动的重要原因。

关键词: 股市波动性, 政策性因素, ICSS：MV算法, 非参数GARCH模型, N-W核回归估计

Abstract: In this paper, ICSS: MV algorithm is used to detect structural breakpoints of daily return series of Shanghai composite index and Shenzhen component index from Dec 12, 1996 to Dec 31, 2010. By corresponding structural breakpoints and major events, all the policy events among the major events are selected, and then according to these policy events, the sample period is divided into thirteen sub-intervals. In order to avoid the shortfalls caused by misspecification using parametric models, nonparametric GARCH model is used to estimate the volatility. Finally, N-W kernel regression of the volatility and the daily return is conducted, and furthermore the policy factors that cause the structural volatility of stock market are analyzed. It is concluded that the adjustment of the benchmark interest rate of deposit and loan as well as the deposit reserve ratio, the reduction of the state-owned shares, the permission of institutional investors to trade mutual funds, the adjustment of the stamp duty and so on are policy impact factors that cause structural volatility of Shanghai and Shenzhen stock market.

Key words: stock market volatility, policy factors, ICSS：MV algorithm, nonparametric GARCH model, N-W kernel regression

中图分类号:

F830.91

杨继平, 陈晓暄, 张春会. 中国沪深股市结构性波动的政策性影响因素[J]. 中国管理科学, 2012, 20(6): 43-51.

YANG Ji-ping, CHEN Xiao-xuan, ZHANG Chun-hui. Policy Impact Factors on the Volatility of Shanghai and Shenzhen Stock Market in China[J]. Chinese Journal of Management Science, 2012, 20(6): 43-51.

参考文献

[1] 邹昊平, 唐利民, 袁国良. 政策性因素对中国股市的影响:政府与股市投资者的博弈分析[J]. 世界经济, 2000, 11: 20-28.
[2] 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: 63-71.
[3] 范南, 王礼平. 我国印花税变动对证券市场波动性影响实证研究[J]. 金融研究, 2003, 6: 38-45.
[4] 毕晓文, 冯玉梅. 利率调整对我国股市波动性的影响研究—基于2004年利率上调的实证分析[J]. 内蒙古科技与经济, 2007,(2): 18-23.
[5] 方舟, 倪玉娟, 庄金良. 货币政策冲击对股票市场流动性的影响——基于Markov区制转换VAR模型的实证研究[J]. 金融研究, 2011, 7: 43-56.
[6] Basistha A, Kurov A. Macroeconomic cycles and the stock market’s reaction to monetary policy[J]. Journal of Banking & Finance, 2008, 32: 2606-2616.
[7] Kurov A. Investor sentiment and the stock market’s reaction to monetary policy[J]. Journal of Banking & Finance, 2010, 34: 139-149.
[8] Laopodis N T. Fiscal policy and stock market efficiency: evidence for the United States[J]. The Quarterly Review of Economics and Finance, 2009, 49: 633-650.
[9] Brown R L, Durbin J, Evans J M. Techniques for testing the constancy of regression relationships over time[J]. Journal of Royal Statistical Society, 1975, 37: 149-192.
[10] Incan C, Tiao G C. Use of cumulative sums of squares for retrospective detection of change of variance[J]. Journal of the American Statistical Association, 1994, 89: 913-923.
[11] Huang B N,Yang C W. The impact of settlement time on the volatility of stock market revisited: an application of the iterated cumulative sums of squares detection method for changes of variance[J]. Applied Economic Letters, 2001, 8(10): 665-668.
[12] Bos T, Hoontrakul P. Estimation of mean and variance episodes in the price return of the stock exchange of Thailand[J]. Financial Risk and Financial Management, 2002, 16: 535-554.
[13] 赵昌文, 杜江, 杨记军. 中国股市股指收益序列的结构性变点与重大事件反应——基于ICSS: MV算法的实证研究. 第四届中国经济学年会论文集, 2004.
[14] 杨成. 我国证券市场结构突变与波动性实证研究[J]. 南京师大学报, 2009, 3: 57-62.
[15] Alfreedi A A, Isa Z, Hassan A. Regime shifts in asymmetric GARCH models assuming heavy-tailed distribution: evidence from GCC stock markets[J]. Journal of Statistical and Econometric Methods, 2012, 1: 43-76.
[16] 鲁万波. 基于非参数GARCH模型的中国股市波动性预测[J]. 数理统计与管理, 2006, 25: 455-461.
[17] Bühlmann P, McNeil A J. An algorithm for nonparametric GARCH modeling[J]. Computational Statistics & Data Analysis, 2002, 40: 665-683.
[18] 鲁万波, 李竹渝. 股票收益率波动性的非参数核回归估计及对中国股市的实证分析[J]. 高校应用数学学报A辑, 2005, 20: 9-16.
[19] 班耀波, 齐春宇. 机构投资者:稳定市场还是加剧波动[J]. 经济评论, 2003,(6): 94-98.
[20] 徐东, 刘志阳, 徐奉臻. 我国证券投资基金羊群行为的实证分析(1999-2004)——基于LSV和时间序列的研究[J]. 哈尔滨工业大学学报, 2006,(12): 2132-2139.