Comparative Study of Backpropagation Algorithms in Forecasting Volatility of Crude Oil Price in Nigeria

S. Suleiman; S. U. Gulumbe; B. K. Asare; M. Abubakar

doi:doi:10.11648/j.sjams.20160403.11

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Comparative Study of Backpropagation Algorithms in Forecasting Volatility of Crude Oil Price in Nigeria

S. Suleiman, S. U. Gulumbe, B. K. Asare, M. Abubakar

Published in Science Journal of Applied Mathematics and Statistics (Volume 4, Issue 3)

Received: 5 April 2016 Accepted: 19 April 2016 Published: 7 May 2016

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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.

Published in	Science Journal of Applied Mathematics and Statistics (Volume 4, Issue 3)
DOI	10.11648/j.sjams.20160403.11
Page(s)	88-96
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

GARH Models, Recurrent Neural Networks, Backpropagation Algorithms and Forecasting

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APA Style

S. Suleiman, S. U. Gulumbe, B. K. Asare, M. Abubakar. (2016). Comparative Study of Backpropagation Algorithms in Forecasting Volatility of Crude Oil Price in Nigeria. Science Journal of Applied Mathematics and Statistics, 4(3), 88-96. https://doi.org/10.11648/j.sjams.20160403.11

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ACS Style

S. Suleiman; S. U. Gulumbe; B. K. Asare; M. Abubakar. Comparative Study of Backpropagation Algorithms in Forecasting Volatility of Crude Oil Price in Nigeria. Sci. J. Appl. Math. Stat. 2016, 4(3), 88-96. doi: 10.11648/j.sjams.20160403.11

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AMA Style

S. Suleiman, S. U. Gulumbe, B. K. Asare, M. Abubakar. Comparative Study of Backpropagation Algorithms in Forecasting Volatility of Crude Oil Price in Nigeria. Sci J Appl Math Stat. 2016;4(3):88-96. doi: 10.11648/j.sjams.20160403.11

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@article{10.11648/j.sjams.20160403.11,
  author = {S. Suleiman and S. U. Gulumbe and B. K. Asare and M. Abubakar},
  title = {Comparative Study of Backpropagation Algorithms in Forecasting Volatility of Crude Oil Price in Nigeria},
  journal = {Science Journal of Applied Mathematics and Statistics},
  volume = {4},
  number = {3},
  pages = {88-96},
  doi = {10.11648/j.sjams.20160403.11},
  url = {https://doi.org/10.11648/j.sjams.20160403.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20160403.11},
  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.},
 year = {2016}
}

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TY  - JOUR
T1  - Comparative Study of Backpropagation Algorithms in Forecasting Volatility of Crude Oil Price in Nigeria
AU  - S. Suleiman
AU  - S. U. Gulumbe
AU  - B. K. Asare
AU  - M. Abubakar
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N1  - https://doi.org/10.11648/j.sjams.20160403.11
DO  - 10.11648/j.sjams.20160403.11
T2  - Science Journal of Applied Mathematics and Statistics
JF  - Science Journal of Applied Mathematics and Statistics
JO  - Science Journal of Applied Mathematics and Statistics
SP  - 88
EP  - 96
PB  - Science Publishing Group
SN  - 2376-9513
UR  - https://doi.org/10.11648/j.sjams.20160403.11
AB  - 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.
VL  - 4
IS  - 3
ER  -

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Author Information

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

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Plain Text BibTeX RIS

APA Style

S. Suleiman, S. U. Gulumbe, B. K. Asare, M. Abubakar. (2016). Comparative Study of Backpropagation Algorithms in Forecasting Volatility of Crude Oil Price in Nigeria. Science Journal of Applied Mathematics and Statistics, 4(3), 88-96. https://doi.org/10.11648/j.sjams.20160403.11

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ACS Style

S. Suleiman; S. U. Gulumbe; B. K. Asare; M. Abubakar. Comparative Study of Backpropagation Algorithms in Forecasting Volatility of Crude Oil Price in Nigeria. Sci. J. Appl. Math. Stat. 2016, 4(3), 88-96. doi: 10.11648/j.sjams.20160403.11

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AMA Style

S. Suleiman, S. U. Gulumbe, B. K. Asare, M. Abubakar. Comparative Study of Backpropagation Algorithms in Forecasting Volatility of Crude Oil Price in Nigeria. Sci J Appl Math Stat. 2016;4(3):88-96. doi: 10.11648/j.sjams.20160403.11

Copy | Download

@article{10.11648/j.sjams.20160403.11,
  author = {S. Suleiman and S. U. Gulumbe and B. K. Asare and M. Abubakar},
  title = {Comparative Study of Backpropagation Algorithms in Forecasting Volatility of Crude Oil Price in Nigeria},
  journal = {Science Journal of Applied Mathematics and Statistics},
  volume = {4},
  number = {3},
  pages = {88-96},
  doi = {10.11648/j.sjams.20160403.11},
  url = {https://doi.org/10.11648/j.sjams.20160403.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20160403.11},
  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.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - Comparative Study of Backpropagation Algorithms in Forecasting Volatility of Crude Oil Price in Nigeria
AU  - S. Suleiman
AU  - S. U. Gulumbe
AU  - B. K. Asare
AU  - M. Abubakar
Y1  - 2016/05/07
PY  - 2016
N1  - https://doi.org/10.11648/j.sjams.20160403.11
DO  - 10.11648/j.sjams.20160403.11
T2  - Science Journal of Applied Mathematics and Statistics
JF  - Science Journal of Applied Mathematics and Statistics
JO  - Science Journal of Applied Mathematics and Statistics
SP  - 88
EP  - 96
PB  - Science Publishing Group
SN  - 2376-9513
UR  - https://doi.org/10.11648/j.sjams.20160403.11
AB  - 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.
VL  - 4
IS  - 3
ER  -

Copy | Download