Reducing Overestimating and Underestimating Volatility via the Augmented Blending-ARCH Model

SVR-GARCH model tends to “backward eavesdrop” when forecasting the financial time series volatility in which case it simply produce prediction by deviating previous volatility. Though has achieved good performance terms of various measurements, trading opportunities, peak or trough behaviors are all hampered underestimating overestimating We propose a blending ARCH (BARCH) and an augmented BARCH (aBARCH) overcome this kind problem make towards better behaviors.The method is illustrated using real data sets including SH300 S&P500. The empirical results obtained suggest that models improve ability.