本文以上证指数的日收益率为样本,对上证指数建立GARCH-VaR模型,比较不同分布假定下GARCH类模型对上证指数波动率的拟合效果,计算并检验上证指数VaR值的预测结果对实际损失的覆盖情况。分析的结果表明,TARCH模型与EGARCH模型更适合测度...本文以上证指数的日收益率为样本,对上证指数建立GARCH-VaR模型,比较不同分布假定下GARCH类模型对上证指数波动率的拟合效果,计算并检验上证指数VaR值的预测结果对实际损失的覆盖情况。分析的结果表明,TARCH模型与EGARCH模型更适合测度上证指数条件方差,且在t分布下,模型能够更好地反映上证指数收益率扰动项的分布特征。进一步,为克服ARMA-GARCH模型在中长期预测中出现的较大误差,使用ARIMA-LSTM模型结合GARCH类模型预测指数波动率,有效提高了GARCH-VaR模型的预测准度。最后,通过TARCH模型,初步检验了我国股市注册制全面推行对上证指数波动率所产生的影响,发现该政策的实施显著降低了上证指数的波动幅度。This article takes the daily return of the Shanghai Composite Index as a sample, establishes a GARCH-VaR model for the Shanghai Composite Index, compares the fitting effect of GARCH models on the volatility of the Shanghai Composite Index under different distribution assumptions, calculates and tests the coverage of actual losses by the predicted VaR value of the Shanghai Composite Index. The analysis results indicate that the TARCH model and EGARCH model are more suitable for measuring the conditional variance of the Shanghai Composite Index, and under the t-distribution, the model can better reflect the distribution characteristics of the disturbance term of the Shanghai Composite Index return. Furthermore, to overcome the significant errors in medium- and long-term forecasting caused by the ARMA-GARCH model, the ARIMA-LSTM model combined with GARCH class models was used to predict index volatility, effectively improving the prediction accuracy of the GARCH-VaR model. Finally, through the TARCH model, the impact of the comprehensive implementation of the registration system in China’s stock market on the volatility of the Shanghai Composite Index was preliminarily examined, and it was found that the implementation of this po展开更多
文摘本文以上证指数的日收益率为样本,对上证指数建立GARCH-VaR模型,比较不同分布假定下GARCH类模型对上证指数波动率的拟合效果,计算并检验上证指数VaR值的预测结果对实际损失的覆盖情况。分析的结果表明,TARCH模型与EGARCH模型更适合测度上证指数条件方差,且在t分布下,模型能够更好地反映上证指数收益率扰动项的分布特征。进一步,为克服ARMA-GARCH模型在中长期预测中出现的较大误差,使用ARIMA-LSTM模型结合GARCH类模型预测指数波动率,有效提高了GARCH-VaR模型的预测准度。最后,通过TARCH模型,初步检验了我国股市注册制全面推行对上证指数波动率所产生的影响,发现该政策的实施显著降低了上证指数的波动幅度。This article takes the daily return of the Shanghai Composite Index as a sample, establishes a GARCH-VaR model for the Shanghai Composite Index, compares the fitting effect of GARCH models on the volatility of the Shanghai Composite Index under different distribution assumptions, calculates and tests the coverage of actual losses by the predicted VaR value of the Shanghai Composite Index. The analysis results indicate that the TARCH model and EGARCH model are more suitable for measuring the conditional variance of the Shanghai Composite Index, and under the t-distribution, the model can better reflect the distribution characteristics of the disturbance term of the Shanghai Composite Index return. Furthermore, to overcome the significant errors in medium- and long-term forecasting caused by the ARMA-GARCH model, the ARIMA-LSTM model combined with GARCH class models was used to predict index volatility, effectively improving the prediction accuracy of the GARCH-VaR model. Finally, through the TARCH model, the impact of the comprehensive implementation of the registration system in China’s stock market on the volatility of the Shanghai Composite Index was preliminarily examined, and it was found that the implementation of this po
