Volume 4, Issue 3, June 2016, Page: 88-96
Comparative Study of Backpropagation Algorithms in Forecasting Volatility of Crude Oil Price in Nigeria
S. Suleiman, Department of Mathematics, Usmanu Danfodiyo University, Sokoto, Nigeria
S. U. Gulumbe, Department of Mathematics, Usmanu Danfodiyo University, Sokoto, Nigeria
B. K. Asare, Department of Mathematics, Usmanu Danfodiyo University, Sokoto, Nigeria
M. Abubakar, Department of Economics, Usmanu Danfodiyo University, Sokoto, Nigeria
Received: Apr. 5, 2016;       Accepted: Apr. 19, 2016;       Published: May 7, 2016
DOI: 10.11648/j.sjams.20160403.11      View  3162      Downloads  117
Abstract
This paper explores the application of artificial neural network in volatility forecasting. A recurrent neural network has been integrated in to GARCH model to form the hybrid model called GARCH-Neural model. The emphasis of the research is to investigate the performance of the variants of Backpropagation algorithms in training the proposed GARCH-neural model. In the first place, EGARCH (3, 3) was identified in this paper most preferred model describing crude oil price volatility in Nigeria. Similarly, Levenberg-Marquardt (LM) training algorithms were found to be fastest in convergence and also provide most accurate predictions of the volatility when to other training techniques.
Keywords
GARH Models, Recurrent Neural Networks, Backpropagation Algorithms and Forecasting
To cite this article
S. Suleiman, S. U. Gulumbe, B. K. Asare, M. Abubakar, Comparative Study of Backpropagation Algorithms in Forecasting Volatility of Crude Oil Price in Nigeria, Science Journal of Applied Mathematics and Statistics. Vol. 4, No. 3, 2016, pp. 88-96. doi: 10.11648/j.sjams.20160403.11
Copyright
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reference
[1]
Adamu A., (2015). The Impact of Global Fall in Oil Prices on the Nigerian Crude Oil Revenue and Its Prices, Proceedings of the Second Middle East Conference on Global Business, Economics, Finance and Banking Dubai-UAE, 22-24.
[2]
Mgbame C. O., Donwa P. A., Onyeokweni O. V., (2015). Impact of oil price volatility on Economic growth: Conceptual perspective, International Journal of Multidisciplinary Research and Development 9, 80-85.
[3]
Ogiri, I., H., Amadi, S., N., Uddin, M., M., & Dubon, P. (2013). Oil price and stock market performance in Nigeria: An empirical analysis. American Journal of Social and Management Sciences, 4 (1), 20–41.
[4]
Oriakhi, D. E., & Osazee, I. D. (2013). Oil price volatility and its consequences on the growth of the Nigerian economy: An examination (1970-2010). Asian Economic and Financial Review, 3 (5), 683-702.
[5]
Halpin, S. M. & Burch, R. F., (1997) “Applicability of neural networks to industrial and commercial power systems: a tutorial overview”, IEEE Trans. Industry Applications, Vol. 33, No. 5, pp 1355-1361.
[6]
Hajizadeh E., Seifi A., Fazel Zarandi M. H., Turksen I. B., (2012). A hybrid modeling approach for forecasting the volatility of S&P 500 index return, Expert Systems with Applications, 39, 431-436.
[7]
Roman, J., and A. Jameel, (1996) “Backpropagation and recurrent neural networks in financial analysis of multiple stock market returns,” Proceedings of the Twenty-Ninth Hawaii International Conference on System Sciences, Vol. 2, 1996, pp. 454–460.
[8]
Monfared S. A., and Enke D., (2014) “Volatility Forecasting using a Hybrid GJR-GARCH Neural Network model”, Procedia Computer Science 36, 246-253.
[9]
Donaldson R. G., and Kamstra M., (1997) “An artificial neural network-GARCH model for international stock return volatility”, Journal of Empirical Finance, vol. 4, no. 1, pp. 17-46.
[10]
Mantri J. K., Gahan P., Nayak B. B., (2010) “Artificial Neural Networks - An Application to Stock Market Volatility”, International Journal of Engineering Science and Technology, vol. 2, no. 5, pp. 1451-1460.
[11]
Jung-W. P., Venayagamoorthy, G. K., & Harley, R. G., (2005) “MLP/RBF neural networks based online global model identification of synchronous generator”, IEEE Trans. Industrial Electronics, Vol. 52, No. 6, pp1685- 1695.
[12]
Venayagamoorthy, G. K., & Kalyani, R. P., (2005) “Two separate continually online-trained neurocontrollers for a unified power flow controller”, IEEE Trans. Industry Applications, Vol. 41, No. 4, pp 906-916.
[13]
Mohamed, Y. A.-R., & El-Saadany, E. F., (2008) “Adaptive Discrete-Time Grid-Voltage Sensorless Interfacing Scheme for Grid-Connected DG-Inverters Based on Neural- Network Identification and Deadbeat Current Regulation”, IEEE Trans. Power Electronics, Vol. 23, No. 1, pp308-321.
[14]
Tiwari, S., Naresh, R., & Jha, R., (2011) “Neural network predictive control of UPFC for improving transient stability performance of power system”, Appl Soft Comput, Vol. 11, No. 8, pp4581-4590.
[15]
Engle, R. F., (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica, 50, 987-1007.
[16]
Bollerslev, T. (1986). Generalized autoregressive conditional heteroscedasticity. Journal of Econometrics, 31, 307–327.
[17]
Glosten, L. R. R. Jagannathan and D. Runkle. 1993. “On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks.” Journal of Finance. 48, 1779-1801.
[18]
Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: a new approach. Econo- metrica 59, 347-370.
[19]
Longmore, R., and W. Robinson. 2004. “Modelling and Forecasting Exchange Rate Dynamics: An Application of Asymmetric Volatility Models”. Bank of Jamaica. Working Paper WP2004/03.
[20]
Ding, Z. R. F. Engle and C. W. J. Granger. 1993. “Long Memory Properties of Stock Market Returns and a New Model”. Journal of Empirical Finance. 1. 83–106.
[21]
Gupta M., Jin L., and Homma N., (2003) Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory. New York: IEEE and Wiley.
[22]
Werbos P. J., The Roots of Backpropagation. New York: Wiley, 1994.
[23]
De Jesus, O. and M. T. Hagan, (2001a). Backpropagation through time for a general class of recurrent network. Proceedings of the international Joint Conference on Neural Networks, July 15-19, Washington, DC, USA., PP: 2638-2642.
[24]
Roman, J., and A. Jameel, “Backpropagation and recurrent neural networks in financial analysis of multiple stock market returns,” Proceedings of the Twenty-Ninth Hawaii International Conference on System Sciences, Vol. 2, 1996, pp. 454–460.
[25]
Kamwa, I., R. Grondin, V. K. Sood, C. Gagnon, Van Thich Nguyen, and J. Mereb, “Recurrent neural networks for phasor detection and adaptive identification in power system control and protection,” IEEE Transactions on Instrumentation and Measurement, Vol. 45, No. 2, 1996, pp. 657–664.
[26]
Medsker, L. R., and L. C. Jain (2000), Recurrent neural networks: design and applications, Boca Raton, FL: CRC Press.
[27]
Narendra, K. S., & Parthasarathy, K., (1990) “Identification and control of dynamical systems using neural networks”, IEEE Trans. Neural Networks, Vol. 1, No. 1, pp4-27.
[28]
Hagan, M. T., Demuth, H. B., & Beale, M. H (1996) Neural Network Design, MA: PWS Publishing Boston.
[29]
Moller, M. F., (1993) “A scaled conjugate gradient algorithm for fast supervised learning”, Neural Networks, Vol. 6, pp 525-533.
[30]
Hagan, M. T., & Menhaj, M., (1994) “Training feed-forward networks with the Marquardt algorithm”, IEEE Trans. Neural Networks, Vol. 5, No. 6, pp989-993.
[31]
Foresee, F. D. & Hagan, M. T., (1997) “Gauss-Newton approximation to Bayesian regularization”, International Joint Conference on Neural Networks.
[32]
Mackay, D. J. C., (1992) “Bayesian interpolation”, Neural Computation, Vol. 4, No. 3, pp 415-447.
[33]
S. U. Gulumbe, S. Suleiman, B. K. Asare and M. Abubakar. Forecasting Volatility of Nigerian Crude Price Using Non-linear Auto-Regressive with Exogenous (NARX) Inputs Model, imperial journal of interdisciplinary Research (IJIR), Vol. 2, Issue 5, pp 434-442.
Browse journals by subject