Development of hybrid algorithm of residual bootstrap artificial neural network based on sukuk volatility forecast model.

Nurul Hila Zainuddin

Abstract

Accuracy estimation is an important issue in time series forecasting field, for example Islamic investment namely sukuk. In order to provide the accuracy estimation of forecasting, the statistical estimator has to be unbiased and has minimum error. In this study, the statistical estimator of moving average model is applied to forecast the sukuk series data. However, the accuracy of this model is questionable and in order to improve the accuracy, this study proposed a hybrid approach using artificial neural network (ANN) algorithm for moving average model. Noted that, the parameter of moving average is selected by considering the AIC, AICc and BIC criterion values, and the selected parameter eventually to be used as input layer of ANN algorithm. In order to examine the forecasting performance, the proposed algorithm used to calculate the 10 days ahead and 15 days ahead forecasting. Based on empirical result, it has shown that, the proposed algorithm helps to reduce the error estimation and eventually improve the forecasting of sukuk investment.

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