稳健AR模型的构建及其在金融时序中的应用

时间序列自回归AR模型在建模过程中易受离群值的影响,导致计算结果与实际不相符。针对这一现象,运用FQn统计量对传统自相关函数进行改进,构建出自回归AR模型的稳健估计算法,以克服离群值的影响,并对此方法进行了模拟和实证分析。模拟和实证分析均表明:当时序数据中不存在离群值时,传统估计方法与稳健估计方法得到的结果基本保持一致;当数据中存在离群值时,运用传统估计方法得到的结果出现较大变化,而运用稳健估计方法得到的结果基本不变.这说明相对于传统估计方法,稳健估计方法能有效抵抗离群值的影响,具有良好的抗干扰性和高抗差性。

The Formation of Robust AR Model and It's Application in Financial Time Series

The AR model of time series were easily be interfered by outliers, which will lead to the outcome of modeling deviates from the fact.In reaction to the phenomenon , we improved the traditional autocorrelation function by utilizing FQn statistic, and put forward the robust algorithm of AR model to avoid the disturbance from outliers, then made the digital simulation and the empirical analysis on the basis of robust algorithm of autocorrelation function.We found that the results of the traditional estimate method were basically the same when there were no outliers in the data;the results from the traditional estimate method changed considerably while robust estimate method remained consistent when there were outliers in the data.This comparison showed that the robust estimate method,which had good anti-interference and high reliability, could efficiently avoid the disturbance from the outliers.