论文标题
通过增强的混合量模型降低高估和低估波动率
Reducing overestimating and underestimating volatility via the augmented blending-ARCH model
论文作者
论文摘要
当预测财务时间序列波动率时，SVR-Garch模型倾向于“向后窃听”，在这种情况下，它倾向于简单地通过偏离先前的波动率来产生预测。尽管SVR-GARCH模型在时间序列中的各种绩效测量，交易机会，高峰或低谷行为方面取得了良好的性能，但由于低估或高估波动率而受到阻碍。我们提出了一个混合拱门（Barch）和一个增强的Barch（ABARCH）模型来克服这种问题，并对更好的峰值或低谷行为进行预测。使用包括SH300和S＆P500在内的真实数据集说明了该方法。获得的经验结果表明，增强和混合模型可以提高波动性预测能力。
SVR-GARCH model tends to "backward eavesdrop" when forecasting the financial time series volatility in which case it tends to simply produce the prediction by deviating the previous volatility. Though the SVR-GARCH model has achieved good performance in terms of various performance measurements, trading opportunities, peak or trough behaviors in the time series are all hampered by underestimating or overestimating the volatility. We propose a blending ARCH (BARCH) and an augmented BARCH (aBARCH) model to overcome this kind of problem and make the prediction towards better peak or trough behaviors. The method is illustrated using real data sets including SH300 and S&P500. The empirical results obtained suggest that the augmented and blending models improve the volatility forecasting ability.
