Nonlinear Neural Network Forecasting Model For Stock Index Option Price: Hybrid Gjr-garch Approach

摘要

This study integrated new hybrid asymmetric volatility approach into artificial neural networks option-pricing model to improve forecasting ability of derivative securities price. Owing to combines the new hybrid asymmetric volatility method can be reduced the stochastic and nonlinearity of the error term sequence and captured the asymmetric volatility simultaneously. Hence, in the ANNS option-pricing model, the results demonstrate that Grey-GJR-GARCH volatility provides higher predictability than other volatility approaches.