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机构地区:[1]上海理工大学管理学院,上海
出 处:《建模与仿真》2023年第6期5187-5195,共9页Modeling and Simulation
摘 要:本文选取我国股票市场中较为有代表性的上证指数作为研究对象,选取2000年1月1日至2022年11月28日以来所有交易日的股票波动情况进行分析。对参数取值多次拟合得到拟合结果较好的GARCH模型,建立上证指数收益波动序列的GARCH模型。再应用Eviews10.0软件中的Forest和已经建立的GARCH模型结合,预测未来上证指数的条件方差。最后基于GARCH模型计算上证指数的预期VaR值,量化上证指数预期金融风险。为投资者和提前预测股票价格波动,此模型也适合不同的股票收益序列,在一定程度上为投资者提前掌握股票价格走势提供参考帮助。In this paper, the Shanghai Stock Index, a representative stock market in China, is selected as the research object, and the stock fluctuations of all trading days from January 1, 2000 to November 28, 2022 are analyzed. The GARCH model with better fitting results is obtained by fitting the parameter values for many times, and the GARCH model of SSE index return volatility series is established. Then the Forest in Eviews10.0 is combined with the established GARCH model to predict the condi-tional variance of Shanghai Composite Index in the future. Finally, the expected VaR value of SSE index is calculated based on GARCH model, and the expected financial risk of SSE index is quanti-fied. For investors and to predict stock price fluctuations in advance, this model is also suitable for different stock return series, and to a certain extent, it provides reference help for investors to grasp the stock price trend in advance.
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