期刊文献+

伽玛核在正序列数据及股票收益率波动概率密度估计中的应用

下载PDF
导出
摘要 应用伽玛核密度估计方法,对正的序列数据进行模拟与实证研究,可避免边界问题。伽玛核灵活性很强,可以随着要估计的位置不同而分配不同的权重。比较伽玛核密度估计与高斯核密度估计的分布,并将这种估计方法运用于纺织和医药两大板块股票收益率波动的实证分析,结果说明:采用伽玛核估计可以很好地避免传统高斯核估计的边界偏差问题,提高了非参数理论对于正序列数据分析的精确度。
作者 高赟玥
机构地区 浙江工商大学
出处 《商业经济》 2010年第19期79-81,共3页 Business & Economy
  • 相关文献

参考文献6

  • 1Taoufik,B., Jeroen,V.,Nonparametric density estimation for positive time series, Computational Statistics and Data Analysis ,2010.
  • 2Sch uster, E., Incorporating support constraints into nonparametric estimators of densities. Communications in Statistics - Theory and Methods 14, 1985.
  • 3Chen, S.,Probability density functions estimation using gamma kernels. Annals of the Institute of Statistical Mathematics 52, 471-480, 2000.
  • 4Fernandez, M., Monteiro, P., Central limit theorem for asymmetric kernel functionals. Annals of the Institute of Statistical Mathematics 57, 425-442, 2005.
  • 5胡小平,赵梅,何建敏.涨跌停板股票收益率的概率密度估计及其应用[J].统计与决策,2008,24(15):36-39. 被引量:1
  • 6许冰.区域板块股票波动风险的概率相关与volatility差异[J].统计与决策,2005,21(11S):18-20. 被引量:1

二级参考文献15

  • 1Olivier R., Olivier S,On the Way to Recovery: A Nonparametric Bias Free Estimation of Recovery Rate Densities [J].Joumal of Banking & Finance ,2004,28.
  • 2Bouezmarni, T., Rolin, J.-M. Consistency of Beta Kernel Density Function Estimator[J]. Canadian Journal of Statistics, forthcoming, 2001.
  • 3Bouezmarni, T., Scaillet, O. Consistency of Asymmetric Kernel Density Estimators and Smoothed Histograms with Application to Income Data[W]. UCL Institut de Statistique DP, 2003.
  • 4Chen, S., Beta Kernel Estimators for Density Functions [J]. Computational Statistics & Data Analysis,1999, 31.
  • 5Chen, S., Probability Density Function Estimation using Gamma Kernels [J]. Annals of the Institute of Statistical Mathematics , 2000,52.
  • 6Scaillet, O. Density Estimation using Inverse and Reciprocal Inverse Gaussian Kernels [J]. Journal of Nonparametric Statistics, forthcoming,2001.
  • 7Christian Schittenkopf and Georg Dorffner:Risk-Neutral Density Extraction from Option Prices: Improved Pricing with Mixture Density Networks [J].Ieee Transactions on Neural Networks,2001, 7.
  • 8Elion C. , Weigend S. Heinz Z.,Computing Portfolio Risk using Gaussian Mixtures and Independent Component Analysis[J].http:// www.stem.nyu.edu/-aweigend,2003.
  • 9Rosario N. Mantegna ,H. Eugene Stanley ,Modeling of Financial data:Comparison of the Truncated Levy Flight and the ARCH(I) and GARCH(1,1) Processes[J],Physica A,1998, 254.
  • 10Hari M. Gupta, Jose R. Campanha, The Gradually Truncated Levy Flight for Systems with Power-Law Distributions[J]. Physica A,1999, 268.

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