Financial Prediction Applications Using Quantum-Minimized Composite Model ASVR/NGARCH

Adaptive support vector regression (ASVR) applied to the forecast of complex time series is superior to the other traditional prediction methods. However, the effect of volatility clustering occurred in time-series actually deteriorates ASVR prediction accuracy. Therefore, incorporating nonlinear generalized autoregressive conditional heteroscedasticity (NGARCH) model into ASVR is employed for dealing with the problem of volatility clustering to best fit the forecasts. Interestingly, quantum-based minimization algorithm is in this study fo proposed r tuning the resulting weighted-average between ASVR and NGARCH in such a way that the composite model ASVR/NGARCH can achieve the best accuracy of prediction. Quantum optimization here tackles so-called NP-completeness problem and outperforms real-coded genetic algorithm on the same problem due to the optimal or near-optimal weighted-values obtained over the search space.