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dc.contributor.author苗晓宇
dc.date.accessioned2016-05-17T03:20:13Z
dc.date.available2016-05-17T03:20:13Z
dc.date.issued2011
dc.identifier.citation山西财经大学学报,2011,(2):35-42
dc.identifier.issn1007-9556
dc.identifier.otherSXCJ201102006
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/111746
dc.description.abstract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。
dc.description.abstractFirstly,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.
dc.language.isozh_CN
dc.subject风险测度
dc.subject高频数据
dc.subject已实现波动
dc.subjectUHF-GARCH模型
dc.subjectrisk measure
dc.subjecthigh frequency data
dc.subjectrealized volatility
dc.subjectUHF-GARCH
dc.title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
dc.title.alternativeMeasuring Financial Risks Using Different Frequency Data——Based on Ultra-high Frequency,High Frequency and Low Frequency Data Model
dc.typeArticle


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