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

Nurul Hila Zainuddin

QR Code Link :
Type :	research_report
Subject :	QA Mathematics
Main Author :	Nurul Hila Zainuddin
Additional Authors :	Shazlyn Milleana Shaharudin Nurul Akmal Mohamed
Title :	Development of hybrid algorithm of residual bootstrap artificial neural network based on sukuk volatility forecast model.

Place of Production :	Tanjong Malim
Publisher :	Fakulti Sains dan Matematik
Year of Publication :	2018
Corporate Name :	Universiti Pendidikan Sultan Idris

Abstract : Universiti Pendidikan Sultan Idris

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.

References

Khashei, M., Bijari, M., A New Hybrid Methodology for Nonlinear Time Series Forecasting Modelling and Simulation in Engineering, 2011 (2010) 1-6

Khashei, M., Bijari, M., An artificial neural network (p,d,q) model for time series forecasting. Expert Systems with Applicationas, 37 (2011) 479-489

Muhamad Safiih, L, Fuzzy Parametric Sample Selection Model: Monte Carlo Simulation Approach. Journal of Statistical Computation and Simulation, 83(6) (2003) 992-1006.

Muhamad Safiih, L, Anton .A. K., Abu Osman, M.T., Estimated and Analysis of The Relationship Between The Endogenous and Exogenous Variables Using Fuzzy Semi-Parametric Sample Selection Model. American Journal of Applied Sciences, 11(9) (2014) 1542-1 552.

Zhou, Z. J., Hu, C. H., An effective hybrid approach based on grey and ARMA for forecasting gyro drift. Chaos, Solitons and Fractals, 35 (2008) 525–529

Nanda, S.K., Tripathy, D. P., Nayak, S.K., Mohapatra, S., Prediction of Rainfall in India using Artificial Neural Network (ANN) Models. I. J. Intelligent Systems and Applications, 12 (2013) 1-22

Taskaya, T., Casey, M. C., A comparative study of autoregressive neural network hybrids. Neural Networks, 18 (2005) 781–789.

Box, P., Jenkins, G. M., Time series analysis: Forecasting and control. San Francisco, CA: Holden-day Inc, 1976.

Wang, X., Meng, M., A hybrid neural network and arima model for energy consumption forecasting. Journal of Computers, 7(5) (2012) 1184 – 1190.

Zhang, G., Patuwo, B. E., Hu, M. Y., Forecasting with artificial neural networks: the state of the art. International Journal of Forecasting, 14 (1998) 35–62.

Zhang, G. P.,Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50 (2003) 159–175.

Muhamad Safiih, L, Wan Saliha, W. A., Hila, N. Z., Sample selection model with bootstrap (BPSS) approach: case study of the Malaysian population and family survey. Open Journal of Statistics, 6 (2016) 741-748.

Efron, B., Bootstrap Methods: Another Look At the Jackknife. The Annals of Statistics, 7(1) (1979) 1-26.

Efron, B.,Tibshirani, R. J., An Introduction to the Bootstrap. New York, London: Chapman & Hall, (1993).

Dougles, S., Bootstrap confidence intervals in a switching regressions model. Economic Letters, 53 (1996) 7-15.

Hila, N. Z., Muhamad Safiih, L., Modelling moving centerline exponentially weighted moving average (MCEWMA) with bootstrap approach: case study on sukuk musyarakah of Rantau Abang Capital Berhad Malaysia. International Journal of Applied, Business and Economic Research, 14(2) (2016) 621-638.

Rafiei, M., Nikman, T., Aghaei, J. Shafie-khah, M., probabilistic load forecasting using an improved wavelet neural network trained by generalized extreme learning machine. IEEE Transactions on Industrial Informatics, 13(1) (2017) 71-79.

Wang, X., Zhang, F., Ding, J., Kung, H., Latif, A., Jhonson, V. C., Estimation of soil salt content (SSC) in the ebinur lake wetland national nature reserve (ELWNNR), northwest China, based on a bootstrap-bp neural network model and optimal spectral indices. Science of The Total Environment, 615 (2018) 918-930.

Oliveira, E. M., Oliveira, F. L. C., Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods. Energy, 144 (1) (2018) 776-788.

This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials.
You may use the digitized material for private study, scholarship, or research.

Back to previous page