NEW FORECASTING SCHEME USING QUANTUM MINIMIZATION TO REGULARIZE A COMPOSITE OF PREDICTION AND ITS NONLINEAR HETEROSCEDASTICITY

A hybrid BPNN-weighted GREY-C3LSP prediction (BWGC) is performed very well for resolving the overshoot phenomenon significantly, but it definitely deterio- rates the predictive accuracy once the volatility clustering occurs. Thus, we propose a new scheme to incorporate non-linear generalized autoregressive conditional heteroscedasticity (NGARCH) into BWGC prediction, which is optimally regularized by quantum minimiza- tion (QM), in such a way that the composite model, BWGC/NGARCH, can overcome the problem of volatility clustering substantially. In comparison with forecasting perfor- mance of several notable prediction methods, the proposed one can get the best predictive accuracy in two typical experiments. Keywords: Hybrid BPNN-weighted GREY-C3LSP prediction, Non-linear generalized autoregressive conditional heteroscedasticity, Quantum minimization