  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.
