Abstract
A novel online forecasting system on financial option prices is implemented in this paper by combining a particle filter with a neural network. The particle filter (PF) model is based on the Black-Sholes (BS) model for prediction, while the neural network is employed to capture the nonlinear residuals between the actual option prices and the PF predictions. Taking the transaction data of option prices on the Taiwan composite stock index (TWSI), we found that the forecasting performance of the hybrid model is superior to the traditional extended Kalman filter model and the PF model. Our results can help investors make a good hedge on their option positions.
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