Reducing overestimating and underestimating volatility via the augmented blending-ARCH model
This work addresses volatility forecasting issues in financial markets, but it appears incremental as it builds on existing ARCH/GARCH models with modifications.
The paper tackled the problem of SVR-GARCH models overestimating or underestimating volatility in financial time series, which hampers trading opportunities and peak/trough behaviors, by proposing blending ARCH (BARCH) and augmented BARCH (aBARCH) models, with empirical results on SH300 and S&P500 datasets showing improved volatility forecasting ability.
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.