Comparative Study of Backpropagation Algorithms in Forecasting Volatility of Crude Oil Price in Nigeria
Science Journal of Applied Mathematics and Statistics
Volume 4, Issue 3, June 2016, Pages: 88-96
Received: Apr. 5, 2016;
Accepted: Apr. 19, 2016;
Published: May 7, 2016
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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
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.
S. U. Gulumbe,
B. K. Asare,
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.
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