摘要
时间序列模型在股票价格的分析与预测中有着极其重要的应用。本文针对沪深300日收益率建立了ARIMA-GARCH拟合模型。首先对数据进行对数处理、平稳性检验、自相关检验、偏自相关检验和ARCH效应检验，然后消除条件异方差性，最后通过实证分析得到了模型的有效性与准确性。
In this paper, we build ARIMA-GARCH for 300 days＇ daily return rate in Shanghai and Shenzhen. Firstly, we take logarithm stationary test, autocorrelation test, partial autocorrelation test and ARCH＇s effect test to the data. Secondly, we choose the appropriate model to eliminate the conditional heteroscedasticity. Finally, the empirical analysis has been conducted and the results show that the model is effective and accurate.
作者
常月
冯宇旭
曹显兵
CHANG Yue;FENG Yu-xu;CAO Xian-bing(School of Science,Beijing Technology and Business University,Beijing 100048,China)
出处
《数学的实践与认识》
北大核心
2018年第22期21-26,共6页
Mathematics in Practice and Theory
基金
2017年研究生科研能力提升计划项目资助
