基于GARCH模型的上证指数波动性实证分析
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摘要
文章借助于一般的自回归条件异方差模型(GARCH模型),利用上证综指2008年10月至2018年9月的日收盘价数据,对其日收益率的波动(条件方差)进行实证分析.特别采用GARCH(1,1)模型来捕捉上证指数日收益中的波动性.结果表明,ARCH和GARCH的估计系数非常显著,且具有预期的特征,验证持续性波动聚集的存在,并且上证指数收盘价的日收益率波动很大程度上受到前一段时间有关波动性和滞后波动性的消息影响.这一结果证实,上证指数日收益的持续高波动性,使得投资者投资股市的风险加大,政府和股票监管机构应采取适当措施.
Based on the general autoregressive conditional heteroscedasticity model(GARCH model)and the daily closing price data of Shanghai composite index from October 2008 to September 2018,this paper makes an empirical analysis on the fluctuation of daily return(conditional variance). In particular,GARCH(1,1)model is used to capture the volatility of daily returns of the Shanghai stock exchange index. The results show that the estimated coefficients of ARCH and GARCH are very significant and have the expected characteristics,and the existence of persistent volatility aggregation is verified.Moreover,the daily return volatility of Shanghai stock index closing price is largely affected by the previous information about volatility and lagging volatility.This result confirms the continuing high volatility of daily returns of the Shanghai stock exchange index,which increases the risk of investors investing in the stock market.The government and the stock regulatory authorities should take appropriate measures.
Based on the general autoregressive conditional heteroscedasticity model(GARCH model)and the daily closing price data of Shanghai composite index from October 2008 to September 2018,this paper makes an empirical analysis on the fluctuation of daily return(conditional variance). In particular,GARCH(1,1)model is used to capture the volatility of daily returns of the Shanghai stock exchange index. The results show that the estimated coefficients of ARCH and GARCH are very significant and have the expected characteristics,and the existence of persistent volatility aggregation is verified.Moreover,the daily return volatility of Shanghai stock index closing price is largely affected by the previous information about volatility and lagging volatility.This result confirms the continuing high volatility of daily returns of the Shanghai stock exchange index,which increases the risk of investors investing in the stock market.The government and the stock regulatory authorities should take appropriate measures.
引文
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[11]赵进文,王倩.上证180指数的GARCH族模型仿真研究——对上证300指数的间接实证建模分析[J].财经问题研究,2008(3):47-54.
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[2]Nelson D B. Conditional heteroskedasticity in asset returns a new approach[J]. Econometrica,1991(59):352-370.
[3]ENGLE R F,VICTOR K N,ROTHSCHILD M. Asset pricing with a factor ARCH covariance structure:empirical estimates for treasury bills[J]. Journal of Econometrics,1990(45):210-236.
[4]AHMED A E M,SULIMAN S Z. Modelling stock market volatility using GARCH models evidence from sudan[J]. International Journal of Business and Social Science,2011,2(23):114-128.
[5]夏庆.非对称GARCH模型在中国股市收益波动中的研究与应用[D].武汉:武汉理工大学,2005.
[6]陆蓉,徐龙炳.“牛市”和“熊市”对信息的不平衡性反应研究[J].经济研究,2004(3):65-72.
[7]严俊宏.基于投资者情绪的股市波动非对称性研究[J].技术与市场,2013(5):333-335.
[8]NAJIAR D A L.Modelling and estimation of volatility using ARCH/GARCH models in Jordan′s stock marke[J]. Asian Journal of Finance&Accounting,2016,8(1):152-167.
[9]WILK M B,GNANADESIKAN R. Probability plotting methods for the analysis of data[J]. Biometrika,1968,55(1):1-17.
[10]王耀. GARCH模型在计算上海股市风险价值中的应用研究[J].经济问题探索,2007(8):153-157.
[11]赵进文,王倩.上证180指数的GARCH族模型仿真研究——对上证300指数的间接实证建模分析[J].财经问题研究,2008(3):47-54.
[12]詹姆斯D汉密尔顿.时间序列分析[M].北京:中国社会科学出版社,1999.
[13]王燕.应用时间序列分析[M].北京:中国人民大学出版社,2015.
[14]高铁梅,王金明,梁云芳,等.计量经济分析方法与建模[M].北京:清华大学出版社,2009.
[15]杨惟舒.基于上证指数的中国股市ARCH效应实证分析[J].会计与金融,2011(6):22-23.