运用最小二乘支持向量回归机和CARRX模型对股市波动率的预测被引量：3

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摘要条件自回归极差模型(CARRX)是一类新的描述波动率的模型。为了提高CARRX类模型的预测精度,文章将最小二乘支持向量回归机(LSSVR)应用于CARRX模型。先将CARRX模型转化成ARMAX形式,再利用LSSVR对ARMAX模型的参数进行估计(LSSVR-ARMAX)。通过对沪深300指数的预测实证分析,发现无论是采用直接预测还是迭代预测,LSSVR-ARMAX模型的样本外预测能力均优于Perez-Cruz(2003)提出的方法;LSSVR的估计方法能够在长期预测中捕捉到极差波动率的变动趋势,而CARRX类模型对中短期极差波动率的预测准确度较高。

作者耿立艳马军海

机构地区天津大学管理学院

出处《统计与决策》 CSSCI 北大核心 2008年第13期48-50,共3页 Statistics & Decision

关键词最小二乘支持向量回归机条件自回归极差模型波动率

分类号 TP183 [自动化与计算机技术—控制理论与控制工程] F830 [经济管理—金融学]

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参考文献14

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二级参考文献17

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