基于时间序列模型的上证A股指数实证分析  

Empirical Analysis of Shanghai A Shares Index Based on Time Series Model

作　　者：宋雅晴 康晴晴 刘兮 Song Yaqing;Kang Qingqing;Liu Xi(School of Mathematics and Statistics,Hefei Normal University,Hefei 230601,China)

机构地区：[1]合肥师范学院数学与统计学院,合肥230601

出　　处：《黑龙江科学》2022年第17期26-28,共3页Heilongjiang Science

基　　金：国家自然科学基金(71901088);安徽省自然科学基金青年基金项目(1808085QG221);安徽省高校自然科学研究项目(KJ2020A0121);安徽省高校优秀人才支持计划项目(gxyq2020041)。

摘　　要：选取20172021年上证A股指数,运用R语言和EVIEWS软件,拟建了ARIMA模型及GARCH簇模型,并进行短期估测,筛选出最佳模型——ARIMA-EGARCH综合模型。通过综合分析水平与波动两方面,得出上证A股指数是非平稳的,差分序列的偏自相关系数明显拖尾,ARIMA(2,1,2)模型残差序列存在异方差性,上证A股指数未遭受风险的显著影响。为改善我国股市环境,有效规避股市风险,提出了相关建议。The study selects Shanghai Stock Exchange A shares index from 2017 to 2021,applies R language and EVIEWS software,proposes ARIMA model and GARCH cluster model,makes short-term prediction,and selects optimal model:ARIMA-EGARCH comprehensive model.Through the comprehensive analysis of two aspects:level and fluctuation,Shanghai Stock Exchange A shares index are unstable,the partial autocorrelation coefficient of difference sequence is obviously trailing.There is difference in residual sequence in ARIMA(2,1,2)model.There is no significant influence of Shanghai A shares index.Related suggestions are proposed to improve China’s stock market environment,and effectively avoid stock risk.

关 键 词：时间序列模型 上证A股指数 ARIMA-EARCH模型 

分 类 号：F832.51[经济管理—金融学] F224