期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

基于“已实现”波动率的ARFIMA模型预测实证研究 被引量：3

The Empirical Study on the Forecast of ARFIMA Model Based on Realized Volatility
原文传递
导出
摘要 本文采用二次移动平均方法平衡影响"已实现"波动率预测精度的测量误差和市场微观结构误差,利用沪深300指数高频数据实证研究,结果表明"已实现"波动率序列的分布是非正态分布且具有长记忆性,对数"已实现"波动率序列接近于正态分布;最后建立ARFIMA模型,并对波动率进行了预测研究。 This paper adopts the method of second moving average to balance measurement error and market microstructure error. The results of empirical study of the behavior of SHSZ300 index show that there is long memory for realized volatility se- ries. The realized volatility series follows non-normal distribution while Logarithm realized volatility follows normal distribution. Finally, ARFIMA model is established to study the distribution characteristics of realized volatility and forecast the future volatility.
作者 吴有英 马玉林 赵静
机构地区 山东财经大学会计学院 山东财经大学统计与数理学院 山东财政学院东方学院
出处 《投资研究》 CSSCI 北大核心 2011年第10期153-159,共7页 Review of Investment Studies
基金 山东省高等学校科技计划项目(J09LA16)资助
关键词 "已实现"波动率 最优抽样频率 ARFIMA模型 Realized volatility Optimal sampling frequency ARFIMA model
分类号 F224 [经济管理—国民经济] F832.51 [经济管理—金融学]
  • 相关文献

参考文献10

  • 1黄后川,陈浪南.中国股票市场波动率的高频估计与特性分析[J].经济研究,2003,38(2):75-82. 被引量：39
  • 2Andersen TG and Tim Bollerslev, etal,2001,"The Distribution of Stock Return Volatility",Journal of Financial Economic,61, pp. 43-76.
  • 3Andersen TG and Tim Bollerslev, et al,2003, "Modelling and Forecasting Realized Volatility",Econometrica,71(2), pp.579-625.
  • 4Andersen. T.G., T. Bollerslev, and N. Meddahi,2005, "Correcting the Errors: Volatility Forecast Evaluation Using High-Frequency Data and Realized Volatilities",Econometrica,73,pp.279-296.
  • 5Bollerslev, T.and J.H.Wright,2001,"Volatility Forecasting, High-Frequency Data, and Frequency Domain lnference",Review of Economic and Statistics,83,pp.596-602.
  • 6Fleming J,Kirby C,Ostdiek B,2003,"The Economic Value of Volatility Time Using Realized Volatility",Journal of Financial Economic,67,pp.473-509.
  • 7Geweke J and Porter-Hudak S,1983,"The Estimation and Application of Long Memory Time Series Models",Joumal of Time Series Analysis,4,pp.221-238.
  • 8Granger CW J, Joyeux R,1980,"An Introduction to Long-memory Time Series Models and Fractional Differencing",Journal of Time Series Analysis, 1,pp.15-29.
  • 9J.R.M. Hosking, 1981,"Fractional Differencing", Biometrica, 68,pp.165-176.
  • 10Oomen Roel CA, 2002, "Modelling Realized Variance When Returns Are Series Correlated", http:www.iue.it/Personal/Researchers/oomen/oomen02b.

二级参考文献19

  • 1陆懋祖.《高等时间序列经济计量学》[M].上海人民出版社,1998..
  • 2Andersen T. G., Bollerslev T., Diebold F. X., and Labys P. 2001, "Modeling and Forecasting Realized Volatility. "
  • 3Baillie R.T., Bollerslev T. and Mikkelsen H.O. 1996, "Fractional Integrated Generalized Autoregressive Conditional Heteroskedasticity," Journal of Econometrics 74 : 3-30.
  • 4Bollerslev T. 1986, "Generalized Autoregressive Conditional Heteroskedasticity", Journal of Econometrics, 31, 307-327.
  • 5Bollerslev T. and Mikkelsen H. 1996, "Modeling and Pricing Long-Memory in Stock Market Volatility, " Journal of Econometrics, 73, 151-184.
  • 6Chung C.F. and Baillie R.T. 1993, "Small Sample Bias in Conditional Sum-of-Squares Estimator of Fractionally Integrated ARMA Models", Empirical Economics 18: 791-806.
  • 7Doornik J.A. and Ooms M. 2001, "Computational Aspects of Maximum Likelihood Estimation of Autoregressive Fractionally Integrated Moving Average models," Economic Papers 2001-W27/Economics Group, Nuffield College, University of Oxford.
  • 8Ebens H. 1999, "Realized Stock Volatility," The Johns Hopkins University.
  • 9Engle R. F. 1982, "Autoregressive Conditional Heteroskedasticity with Estimates of the Variance for U. K. Inflation". Econometrica 50:987-1008.
  • 10Engle R.F. and T. Bollerslev 1986, "Modelling the Persistence of Conditional Variances," Econometric Reviews, 5, 1-50.

共引文献38

同被引文献38

  • 1戴颖杰.住宅价格的长记忆波动特性实证分析[J].云南财经大学学报（社会科学版）,2012(3):33-36. 被引量：1
  • 2何兴强,李涛.不同市场态势下股票市场的非对称反应——基于中国上证股市的实证分析[J].金融研究,2007(08A):131-140. 被引量：49
  • 3Andersen, T.G., T. Bollerslev, F.X. Diebold, and P. Laby, 2003, "Modeling and forecasting realized volatility", Econometrica, 2 (71), pp. 579-625.
  • 4Barndor-Nielsen, O. E., P. Hansen, A. Lunde, and N. Shephard, 2011, "Multivariate realised kernels: consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading", Journal of Econometrics, (162) pp.149 - 169.
  • 5Bonato, M. Caporin, A. Ranaldo, 2011, "A forecast-based comparison of restricted Wishart autoregressive models for realized eovariance matrices", The European Journal of Finance, 8.
  • 6Campbell, Huisman, and Koedijk2001,"Optimal portfolio selection in a Value-at-Risk framework", Journal of Banking and Finance, (29) pp. 3181-3185.
  • 7De Pooter, M., M. P. Martens, and D. J. Van Dijk, 2008, "Predicting the daily covariance matrix for S&P 100 stocks using intraday data - but which frequency to use? ", Econometric Reviews, 27 pp.199 - 229.
  • 8Engle, R., & Colacito, R., 2005, "Testing and valuing dynamic correlations for asset allocation", Journal of Business & Economic Statistics, 24(2) pp. 238-253.
  • 9Jean-Paul Chavas, 2004, "Risk Analysis in Theory and Practice", Academic Press, London.
  • 10Fleming, J., C. Kirby, and B. Ostdiek, 2003, "The economic value of volatility timing using realized volatility", Journal of Financial Economics, (67) pp. 473 - 509.

引证文献3

二级引证文献5

投资研究
2011年 第10期

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