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不同频率数据在金融市场VaR测度中的对比研究——基于低频、高频与超高频数据模型 被引量：3

Measuring Financial Risks Using Different Frequency Data——Based on Ultra-high Frequency,High Frequency and Low Frequency Data Model
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摘要 首先计算了已实现波动率和超高频波动率,然后使用ARFIMA(0,d,0)-SKST模型计算了条件波动,最后对条件波动调整后的收益率进行了拟合并计算出了VaR值。实证结果发现,使用高频数据甚至超高频数据测量金融风险的准确性并不比低频数据高很多,如果选用模型恰当,完全能够使用低频数据得到高频数据的精度。 Firstly, this paper calculate the realized volatility and the ultra-high frequency volatility, and then use the ARFIMA （0,d,0）-SKST model to calculate the conditional volatility, finally the author calculates and compare the VAR which was calculated by asset return adjusted by conditional volatility. The empirical results show that the use of high-frequency data and even ultra-high frequency data did not improve the accuracy of measurement of financial risk significantly, if selected sensibly, using low frequency data can also get the precision of high-frequency data, the article finally analyzes the applicability of the high- frequency data.
作者 苗晓宇
机构地区 厦门大学计划统计系 吉林财经大学统计学院
出处 《山西财经大学学报》 CSSCI 北大核心 2011年第2期30-37,共8页 Journal of Shanxi University of Finance and Economics
关键词 风险测度 高频数据 已实现波动 UHF-GARCH模型 risk measure high frequency data realized volatility UHF-GARCH
分类号 F830.91 [经济管理—金融学]
  • 相关文献

参考文献13

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二级参考文献41

  • 1徐国祥,金登贵.基于金融高频数据的ACD模型非参数设定检验[J].统计研究,2007,24(4):15-18. 被引量：9
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共引文献59

同被引文献24

  • 1曾勇,王志刚,李平.基于高频数据的金融市场微观结构实证研究综述[J].系统工程,2005,23(3):22-28. 被引量：6
  • 2沈兵.汇率收益率的异方差:基于不同频率的风险价值度量[J].广西金融研究,2005(7):3-10. 被引量：2
  • 3徐正国,张世英.高频金融数据“日历效应”的小波神经网络模型分析[J].数学的实践与认识,2007,37(15):1-6. 被引量：4
  • 4Dionne, G. and P. Duchesne, et al. Intraday Value at Risk (IVaR) using tick-by-tick data with applica- tion to the Toronto Stock Exchange [J]. Journal of Empirical Finance,2009,16(5):777-792.
  • 5Engle, R. F. The econometrics of ultra-high-frequen- cy[J]. Econometrica ,2000,68 (1): 1-22.
  • 6Engle, R. F. and J. R. Russell. Forecasting the fre- quency of changes in quoted foreign exchange prices with the autoregressive conditional duration model. Journal of Empirical Finance [J]. 1997,4 (2-3):187- 212.
  • 7Engle, R. F. and J. R. Russell. Autoregressive Con- ditional Duration: A New Model for Irregularly Spaced Transaction Data [J]. Econometrica1998,66 (5):1127- 1162.
  • 8Ghysels, E. and J Jasiak .GARCH for Irregularly Spaced Data: The ACD-GARCH Model [J]. 1998,97 (6).
  • 9Yacine Ait-Sahalia,P A Mykland,L Zhang. How often to sample a continuous-time process in the presence of market microstructure noise[J].The Review of Financial Studies,2005,(02):351-416.
  • 10Cleiton Taufemback,Sergio Da Silva. Spectral analysis informs the proper frequency in the sampling of financial time series data[J].Physica A:Statistical Mechanics and its Applications,2011,(11):2067-2073.

二级引证文献8

山西财经大学学报
2011年 第2期

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