期刊文献+

沪深股市日收益率波动性比较分析 被引量:2

A comparison of stock returns fluctuation between Shanghai and Shenzhen stock markets
下载PDF
导出
摘要 条件异方差是金融时间序列变量的一个典型特征。在分析沪深股市指数数据统计特征的基础上,用GARCH-M模型和门限ARCH(TARCH)模型分析2001年6月到2005年3月期间中国股市收益的波动特性,并对沪深两市进行比较,可以发现沪市内在的不确定水平比深市高,沪市市场上新的正面消息对市场波动性的影响则比深市大。中国股票价格的波动具有明显的杠杆效应,且沪深股票市场杠杆效应不同,当出现"利好消息"时,深市比沪市的冲击大,出现"利空消息"时沪市比深市的冲击大。 The conditional heteroscedasticity is a typical characteristic of finance time series.After summarizing the statistic characteristics of Shanghai and Shenzhen stock market index data this paper employs the GARCH-M model and the threshold ARCH(TARCH)model to analyze China's stock returns characteristics and make a comparison between the two stock markets from June,2001 to March,2005.The findings indicate there are more indefinite elements on Shanghai stock market and it is more easily influenced by positive news.China's stock price fluctuation reflects the obvious release lever effect,though Shanghai and Shenzhen stock markets have different expressions.On facing 'good news',Shenzhen market shows bigger reaction than Shanghai market while Shanghai market reacts more seriously to 'bad news'.
出处 《东南大学学报(哲学社会科学版)》 CSSCI 2007年第5期28-31,共4页 Journal of Southeast University(Philosophy and Social Science)
  • 相关文献

参考文献14

  • 1[1]Engle R F.Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflations[J].Econometrica,1982,50:987-1007.
  • 2[2]Bollerslev T.Generalized autoregressive conditional heterosledasticity[J].Journal of Econometrics,1986,31:309-327.
  • 3[3]Engle R F,David M L,R P Robins.Estimating time varying risk premia in the term structure:the ARCH-M model[J].Econometrica,1987,55:391-407.
  • 4[4]Nelson D B.Conditional heteroscedasticity in asset returns:a new approach[J].Eonometrica,1991,59.347-370.
  • 5[5]Nicholls D F,B G Quinn.Random coefficient autoregressive models:an introduction[M]//Lecture Notes in Statistics.Springer-Verlag:New York,1982.
  • 6[6]Melino A,S M Turnbull.Pricing foreign currency options with stochastic volatility[J].Journal of Econometrics,1990,45:239-265.
  • 7[7]Harvey A C,E Ruiz,N Shephard.Multivariate stochastic variance models[J].Review of Econometric Studies,1994,61:247-264.
  • 8[8]Jacquier E,G N Polson,P Rossi.Bayesian analysis of stochastic variance models[J].Journal of Business & Economic Statistics,1994,12:371-417.
  • 9岳朝龙.上海股市收益率GARCH模型族的实证研究[J].数量经济技术经济研究,2001,18(6):126-126. 被引量:34
  • 10皮天雷.我国沪市波动聚集性GARCH效应的实证研究[J].管理科学,2003,16(6):31-35. 被引量:9

二级参考文献19

  • 1徐剑刚,唐国兴.我国股票市场报酬与波动的GARCH-M模型[J].数量经济技术经济研究,1995,12(12):28-32. 被引量:32
  • 2汤果.FIGARCH模型对股市收益长忆性的实证分析[J].统计研究,2000,(1).
  • 3威廉·格林.经济计量分析(第二版)[M].北京:中国社会科学出版社,1998.620~629.
  • 4Christian Gourieroux. ARCH Models and Financial Applications [ M ]. New York : Springer - verlag, 1997.27 - 28.
  • 5Scrooks C. Predicting Stock Index Volatility: Can Market Volume Help ? [ J ]. Journal of Forcasting, 1997,(17) :59 -80.
  • 6Engel. Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of U. K Inflation [ J ].Econometricca, 1982, (50) :987 - 1008.
  • 7Kroner. ARCH Modeling in Finance [ J ]. Journal of Econometrics, 1992, ( 52 ) :5 - 59.
  • 8Ma Chao - Qun, Chen Mu - Miao. ARCH Model and Its Application in Finance System [ J ]. Human University, 1998,25(5) :108 - 112.
  • 9Walter SA Schwaiger. A Note on GARCH Predictable Variances and Stock Market Efficiency [ J ]. Journal of Banking and Finance, 1995,19(5) :949 - 953.
  • 10Jim Lee. The Inflation and Output Variability Ttradeoff: Evidence from a GARCH Model [ J ]. Economics Letters, 1999,62( 1 ) :63 -67.

共引文献304

同被引文献29

引证文献2

二级引证文献6

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部