This paper examined the monthly volatility of Naira/Dollar exchange rates in Nigeria between the periods of January, 1995 to January, 2016. Forecasting volatility remains to be an important step to be taken in several decision makings involving financial market. Traditional GARH models were usually applied in forecasting volatility of a financial market. This study was aim at enhancing the performance of these models in volatility forecasting in which both the traditional GARCH and Dynamic Neural Networks were hybridized to develop the proposed models offorecasting the volatility of Inflation rate in Nigeria. The values of the volatility estimated by the best fitted GARH model are used as input to the Neural Network. The inputs of the first hybrid model also included past values of other related endogenous variables. The second hybrid model takes as inputs both series of the simulated data and the inputs of the first hybrid model. The forecasts obtained by each of those hybrid models have been compared with those of GARCH model in terms of the actual volatility. The computational results demonstrate that the second hybrid model provides better volatility forecasts.
| Published in | American Journal of Management Science and Engineering (Volume 1, Issue 1) |
| DOI | 10.11648/j.ajmse.20160101.12 |
| Page(s) | 8-14 |
| 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 |
Volatility, ARCH Models, Dynamic Neural Networks, Monthly Standard Deviation
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APA Style
S. Suleiman, S. U. Gulumbe, B. K. Asare, M. Abubakar. (2016). Training Dynamic Neural Networks for Forecasting Naira/Dollar Exchange Returns Volatility in Nigeria. American Journal of Management Science and Engineering, 1(1), 8-14. https://doi.org/10.11648/j.ajmse.20160101.12
ACS Style
S. Suleiman; S. U. Gulumbe; B. K. Asare; M. Abubakar. Training Dynamic Neural Networks for Forecasting Naira/Dollar Exchange Returns Volatility in Nigeria. Am. J. Manag. Sci. Eng. 2016, 1(1), 8-14. doi: 10.11648/j.ajmse.20160101.12
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Download@article{10.11648/j.ajmse.20160101.12,
author = {S. Suleiman and S. U. Gulumbe and B. K. Asare and M. Abubakar},
title = {Training Dynamic Neural Networks for Forecasting Naira/Dollar Exchange Returns Volatility in Nigeria},
journal = {American Journal of Management Science and Engineering},
volume = {1},
number = {1},
pages = {8-14},
doi = {10.11648/j.ajmse.20160101.12},
url = {https://doi.org/10.11648/j.ajmse.20160101.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajmse.20160101.12},
abstract = {This paper examined the monthly volatility of Naira/Dollar exchange rates in Nigeria between the periods of January, 1995 to January, 2016. Forecasting volatility remains to be an important step to be taken in several decision makings involving financial market. Traditional GARH models were usually applied in forecasting volatility of a financial market. This study was aim at enhancing the performance of these models in volatility forecasting in which both the traditional GARCH and Dynamic Neural Networks were hybridized to develop the proposed models offorecasting the volatility of Inflation rate in Nigeria. The values of the volatility estimated by the best fitted GARH model are used as input to the Neural Network. The inputs of the first hybrid model also included past values of other related endogenous variables. The second hybrid model takes as inputs both series of the simulated data and the inputs of the first hybrid model. The forecasts obtained by each of those hybrid models have been compared with those of GARCH model in terms of the actual volatility. The computational results demonstrate that the second hybrid model provides better volatility forecasts.},
year = {2016}
}
TY - JOUR T1 - Training Dynamic Neural Networks for Forecasting Naira/Dollar Exchange Returns Volatility in Nigeria AU - S. Suleiman AU - S. U. Gulumbe AU - B. K. Asare AU - M. Abubakar Y1 - 2016/09/09 PY - 2016 N1 - https://doi.org/10.11648/j.ajmse.20160101.12 DO - 10.11648/j.ajmse.20160101.12 T2 - American Journal of Management Science and Engineering JF - American Journal of Management Science and Engineering JO - American Journal of Management Science and Engineering SP - 8 EP - 14 PB - Science Publishing Group SN - 2575-1379 UR - https://doi.org/10.11648/j.ajmse.20160101.12 AB - This paper examined the monthly volatility of Naira/Dollar exchange rates in Nigeria between the periods of January, 1995 to January, 2016. Forecasting volatility remains to be an important step to be taken in several decision makings involving financial market. Traditional GARH models were usually applied in forecasting volatility of a financial market. This study was aim at enhancing the performance of these models in volatility forecasting in which both the traditional GARCH and Dynamic Neural Networks were hybridized to develop the proposed models offorecasting the volatility of Inflation rate in Nigeria. The values of the volatility estimated by the best fitted GARH model are used as input to the Neural Network. The inputs of the first hybrid model also included past values of other related endogenous variables. The second hybrid model takes as inputs both series of the simulated data and the inputs of the first hybrid model. The forecasts obtained by each of those hybrid models have been compared with those of GARCH model in terms of the actual volatility. The computational results demonstrate that the second hybrid model provides better volatility forecasts. VL - 1 IS - 1 ER -
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