期刊文献+

结构突变下沪深300指数的波动率预测与评价

Forecasting the volatility of CSI 300 Index with Structural Breaks and GARCH models
在线阅读 下载PDF
导出
摘要 考察波动的结构突变性对模型估计和预测能力的影响,并通过SPA检验评估几种GARCH模型的预测能力优劣。研究发现,我国股市收益率的波动在样本期内发生了4次结构突变,波动存在着伪持续现象,且这种突变影响了模型的预测能力;SPA检验表明,短期预测上,经结构突变修正的GARCH模型具有较高的预测能力,验证了结构突变对波动预测的重要性;长期预测上,基于滚动时间窗口下的GARCH模型具有较好的预测能力,通过调整时间窗口的方法来消除结构突变的影响有助于提升模型预测能力。 The author takes account into the influence of the structural breaks into the GARCH model and the forecasting of the volatility, uses the SPA test to evaluate the ability of prediction of several GARCH models. The research shows the evidence of structural breaks in the unconditional variance of the stock returns series over the period and the high levels of persistence in the parameter estimates of the GARCH(1,1) model across the sub-samples. The impact of structural breaks on the accuracy of volatility forecasts has largely been ignored in previous research. The SPA test shows that, in the short-run volatility forecasting, the GARCH model with structural breaks performs best, whereas the GARCH model with rolling windows performs well in the long term prediction. By adjusting the estimation window, it can eliminate the influence of structural breaks and help improve prediction ability.
出处 《西安电子科技大学学报(社会科学版)》 CSSCI 2014年第4期56-64,共9页 Journal of Xidian University:Social Science Edition
基金 西南民族大学中央高校资助项目(2014SZYTD01) 研究生创新科研项目重点项目"结构突变下金融市场间的信息溢出效应分析"(CX2014SZ35)阶段性成果
关键词 结构突变 GARCH 波动预测 股市 SPA检验 Structural Breaks GARCH Volatility Forecasting Stock market SPA test
作者简介 姚宏伟(1988-),男,陕西渭南人,西南民族大学经济学院数量经济学硕士研究生。研究方向为金融计量与金融工程; 蒲成毅(1967-),男,四川绵阳人,西南民族大学经济学院教授,博士生导师,研究方向为风险管理与金融计量。
  • 相关文献

参考文献21

  • 1ENGLE R F.Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation [J].Econometrica,1982,50:987-1007.
  • 2BOLLERSLEV T.Generalized autoregressive conditional heteroskedasticity[J].Journal of Econometrics,1986,31(3):307-327.
  • 3DIEBOLD F X.Modeling the persistence of conditional variance:a comment[J].Econometric Reviews,1986(1):51-56.
  • 4LAMOUREUX C G,LASTRAPES W D.Persistence in Variance,Structural Change,and the GARCH Model[J].Journal of Business and Economic Statistics,1990,8(2):225-234.
  • 5MIKOSCH T,SARICA C.Nonstationarities in financial time series,the long range dependence,and the IGARCH effects [J].Review of Economics and Statistics,2004,86(1):378-390.
  • 6HILLEBRAND E.Neglecting parameter changes in GARCH models [J].Journal of Econometrics,2005,129(1):121-138.
  • 7STARICA C,GRANGER C W J.Nonstationarities in stock returns[J].Review of Economics and Statistics,2005,87:503-522.
  • 8RAPACH D E,STRAUSS J K.Structural breaks and GARCH models of exchange rate volatility[J].Journal of Applied Economics,2008,23(1):382-416.
  • 9BABIKIR A,GUPTA R.Structural breaks and GARCH models of stock return volatility:The case of South Africa[J].Economic Modelling,2012,29:2435-2443.
  • 10AROURI M E,LAHIANI A.Forecasting the conditional volatility of oil spot and futures prices with structural breaks and long memory models[J].Energy Economics,2012,34(1):283-293.

二级参考文献94

共引文献78

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