已实现波动率对股票市场未来收益波动率的预测效果不佳，且其所含有的有效可预测信息也不全面。因此，为提高股票收益波动率的可预测性，引入两个重要预测指标（隐含波动率VIX和WTI（BRT）石油价格波动率）并且建立“大杂烩式”预测自回归模型和“凸组合”预测自回归模型。实证结果表明：结合隐含波动率VIX和WTI（BRT）石油价格波动率的“大杂烩式”预测自回归模型具有很好的预测效果。为进一步证明该结论的可靠性，文章还增加经济周期或改变宏观经济和金融变量等条件。结果表明：相对于WTI（BRT）石油价格波动率，隐含波动率VIX对其他宏观经济和金融变量的可预测性更为有效。最后，基于不同滞后长度和不同宏观经济变量的维度，对各种预测回归模型进行鲁棒性检验。检验结果表明：结合隐含波动率VIX和WTI（BRT）石油价格波动率的自回归模型对股票收益波动率的预测效果更加有效更加稳定。

The realized volatility is not effective in predicting the future return volatility of the stock market, and the effective predictable information contained in it is not comprehensive. Therefore, in order to improve the predictability of stock return volatility, this paper introduces two important predictors（implicit volatility VIX and WTI （BRT） oil price volatility）, and establishes a "kitchen sink" predictive autoregressive model and "convex combined" predictive autoregressive model. The empirical results show that the "kitchen sink" predictive autoregressive model that combines the implied volatility VIX and WTI（BRT） oil price volatility has a good predictive effect. To further prove the reliability of this conclusion, the paper also adds economic cycles or changes conditions such as macroeconomic and financial variables. The results show that, compared to the WTI（BRT） oil price volatility, the implied volatility VIX is more effective for the predictability of other macroeconomic and financial variables. Finally, based on the different lag lengths and the dimensions of different macroeconomic variables, this article conducts robustness tests on various predictive regression models. The test results show that the autoregressive model combined with implied volatility VIX and WTI（BRT） oil price volatility is more effective and stable in predicting stock return volatility.