基于条件异方差模型的基金指数波动率实证分析

Xiayi Zhang
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引用次数: 0

摘要

为了研究中国基金市场的变化,本文以2010年1月至2019年12月上海证券基金指数月度序列数据为基础,对序列进行时间序列建模和拟合预测。EGARCH(1,1)模型对上海证券基金指数序列具有良好的拟合效果,所有参数均不为0,模型的残差序列经检验服从标准正态分布。最后,利用拟合模型对2020年1 - 5月上海证券基金指数进行预测,并与实际值进行比较,检验模型的准确性。结果表明,实际值均在95%置信系数的预测区间内,模型拟合效果较好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Empirical Analysis of Fund Index Volatility Based on Conditional Heteroscedasticity Model
: In order to study the changes of China's fund market, this paper carries out time series modeling and fitting prediction on the series based on the monthly series data of Shanghai Securities Fund Index from January 2010 to December 2019. EGARCH (1,1) model has a good fitting effect on Shanghai Securities Fund Index series, all parameters are not 0, and the residual series of the model is tested to obey the standard normal distribution. Finally, the fitted model is used to predict the Shanghai Securities Fund Index from January to May 2020, and compare it with the real value to test the accuracy of the model. The results show that the actual values are within the prediction interval of 95% confidence coefficient, and the fitting effect of the model is superior.
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