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基于高频数据的波动率建模及应用研究评述 被引量:7

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摘要 囿于数据可得性,传统波动率模型使用的数据频率最高频率一般为日。随着技术进步,更高频率数据越来越引起研究者们的关注。应用高频数据可以在以下诸方面提升人们对于波动率的认识:(1)更好地了解波动率的动态特征;(2)有助于建立新的波动率模型,更准确地预测波动率;(3)作为一个更精确的波动率度量指标用于评价不同模型的预测结果,并为更复杂的模型提供估计工具;(4)能够识别波动率的不同组成部分,为金融理论和实践提供更多的对象和工具。本文就近年来基于高频数据的波动率建模及应用的研究进行评述和总结。
作者 王天一 黄卓
出处 《经济学动态》 CSSCI 北大核心 2012年第3期141-146,共6页 Economic Perspectives
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参考文献39

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同被引文献75

  • 1刘凤芹.基于DIC准则的SV族模型的比较[J].统计与决策,2004,20(9):24-25. 被引量:5
  • 2王春峰,蒋祥林,吴晓霖.随机波动性模型的比较分析[J].系统工程学报,2005,20(2):216-219. 被引量:16
  • 3于亦文.实际波动率与GARCH模型的特征比较分析[J].管理工程学报,2006,20(2):65-69. 被引量:17
  • 4霍光耀,郭名媛.金融波动研究的新进展及未来展望[J].西北农林科技大学学报(社会科学版),2007,7(5):49-55. 被引量:3
  • 5张世英,樊智.2009,《协整理论与波动模型:金融时间序列分析及应用》,清华大学出版社,2009年5月第二版.
  • 6徐国祥.2009,《金融统计学》,上海人民出版社,2009年3月第一版.
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  • 10Bamdorff-Nielsen O. and N. Shephard, 2004, "Power and bipower variation with stochastic volatility and jumps", Journal of Financial Econometrics, 2(1), pp. 1 - 48.

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