%0 Journal Article
%T 函数型非参数部分自回归模型及其在金融中的应用<br>Functional Nonparametric Partial Auto-regression Model and Its Applications in Finance
%A 王咪咪
%A 丁辉&lt
%A br&gt
%A WANG Mi-mi
%A DING Hui
%J 西南大学学报(自然科学版)
%D 2017
%R 10.13718/j.cnki.xdzk.2017.11.014
%X 结合金融市场中的滞后现象以及函数型协变量和响应变量之间的非线性关系提出了函数型非参数部分自回归模型，接着使用profile最小二乘方法和非参数核估计方法给出了该模型的估计，并通过统计模拟验证了该方法的有效性，最后通过上证指数的实例验证了模型的预测能力.<br>Functional data analysis is an important method of analyzing high-frequency data of the financial market. Combining the lag phenomenon on the financial market and the nonlinear relationship between the functional covariate and the response variable, this paper proposes a functional nonparametric partial auto-regression model. Then, the profile least square method and the nonparametric kernel estimation are used to obtain the estimators of the model. Statistical simulation verified its validity. A real example about Shanghai Stock Index data is used to demonstrate the good prediction ability of the model
%K 函数型数据
%K 高频数据
%K 非参数部分自回归模型
%K 核估计&lt
%K br&gt
%K functional data
%K high frequency data
%K partial nonparametric auto-regression model
%K kernel estimation
%U http://xbgjxt.swu.edu.cn/jsuns/html/jsuns/2017/11/201711014.htm