【摘 要】
：
在低频数据领域内,向量GARCH模型和向量SV模型的参数难于准确估计,利用这些模型很难解决多个资产的协方差矩阵的预测问题.向量ARFIMA模型可以对利用高频金融数据计算得到的多
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在低频数据领域内,向量GARCH模型和向量SV模型的参数难于准确估计,利用这些模型很难解决多个资产的协方差矩阵的预测问题.向量ARFIMA模型可以对利用高频金融数据计算得到的多个资产收益的协方差矩阵进行建模,但是随着变量维数的增加,向量ARFIMA模型同样也面临着参数过多而难于准确估计的问题.因此,提出了基于金融高频数据的正交ARFIMA模型.正交ARFIMA模型通过主成分分析将一组变量的协方差矩阵问题转化成了分别考虑它们的主成分的一元波动问题,这样一元的ARFIMA模型可以很直接的得到应用.正交ARFIMA模型通过主成分分析的方法有效的降低了变量的维数,使得其参数估计问题得到很好的解决,对于金融工具的定价、资产配置、风险管理等问题的解决有着深刻的意义.
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