In most of the literature in time series modeling, generalized autoregressive conditional heterosceasticity (GARCH) models has been used as a traditional model to forecast both the economic and financial time series data. Though literature has shown that it is not suitable for non-linear time series. For this reason, this model was augmented with bilinear model in order to make it more relevant in forecasting both economic and financial time series data. After the augmentation, the new model called Bilinear-GARCH (BL-GARCH) shows a better performance based on performance measures indices, models variances and out-of–samples forecast performances. In term of these three criteria the new models outperformed the traditional or classical GARCH model. To drive home this point, these two models were illustrated with Botswana inflation rates data. We observed that the new model (BL-GARCH) outperformed the classical GARCH model.
| Published in | American Journal of Applied Mathematics (Volume 1, Issue 1) |
| DOI | 10.11648/j.ajam.20130101.14 |
| Page(s) | 17-23 |
| 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), 2013. Published by Science Publishing Group |
GARCH Models, BL-GARCH Models, Forecasting, Inflation Rates and Non-Linear
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APA Style
Akintunde Mutairu Oyewale, D. K. Shangodoyin, P. M Kgosi. (2013). Measuring the Forecast Performance of GARCH and Bilinear-GARCH Models in Time Series Data. American Journal of Applied Mathematics, 1(1), 17-23. https://doi.org/10.11648/j.ajam.20130101.14
ACS Style
Akintunde Mutairu Oyewale; D. K. Shangodoyin; P. M Kgosi. Measuring the Forecast Performance of GARCH and Bilinear-GARCH Models in Time Series Data. Am. J. Appl. Math. 2013, 1(1), 17-23. doi: 10.11648/j.ajam.20130101.14
AMA Style
Akintunde Mutairu Oyewale, D. K. Shangodoyin, P. M Kgosi. Measuring the Forecast Performance of GARCH and Bilinear-GARCH Models in Time Series Data. Am J Appl Math. 2013;1(1):17-23. doi: 10.11648/j.ajam.20130101.14
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Download@article{10.11648/j.ajam.20130101.14,
author = {Akintunde Mutairu Oyewale and D. K. Shangodoyin and P. M Kgosi},
title = {Measuring the Forecast Performance of GARCH and Bilinear-GARCH Models in Time Series Data},
journal = {American Journal of Applied Mathematics},
volume = {1},
number = {1},
pages = {17-23},
doi = {10.11648/j.ajam.20130101.14},
url = {https://doi.org/10.11648/j.ajam.20130101.14},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajam.20130101.14},
abstract = {In most of the literature in time series modeling, generalized autoregressive conditional heterosceasticity (GARCH) models has been used as a traditional model to forecast both the economic and financial time series data. Though literature has shown that it is not suitable for non-linear time series. For this reason, this model was augmented with bilinear model in order to make it more relevant in forecasting both economic and financial time series data. After the augmentation, the new model called Bilinear-GARCH (BL-GARCH) shows a better performance based on performance measures indices, models variances and out-of–samples forecast performances. In term of these three criteria the new models outperformed the traditional or classical GARCH model. To drive home this point, these two models were illustrated with Botswana inflation rates data. We observed that the new model (BL-GARCH) outperformed the classical GARCH model.},
year = {2013}
}
TY - JOUR T1 - Measuring the Forecast Performance of GARCH and Bilinear-GARCH Models in Time Series Data AU - Akintunde Mutairu Oyewale AU - D. K. Shangodoyin AU - P. M Kgosi Y1 - 2013/05/30 PY - 2013 N1 - https://doi.org/10.11648/j.ajam.20130101.14 DO - 10.11648/j.ajam.20130101.14 T2 - American Journal of Applied Mathematics JF - American Journal of Applied Mathematics JO - American Journal of Applied Mathematics SP - 17 EP - 23 PB - Science Publishing Group SN - 2330-006X UR - https://doi.org/10.11648/j.ajam.20130101.14 AB - In most of the literature in time series modeling, generalized autoregressive conditional heterosceasticity (GARCH) models has been used as a traditional model to forecast both the economic and financial time series data. Though literature has shown that it is not suitable for non-linear time series. For this reason, this model was augmented with bilinear model in order to make it more relevant in forecasting both economic and financial time series data. After the augmentation, the new model called Bilinear-GARCH (BL-GARCH) shows a better performance based on performance measures indices, models variances and out-of–samples forecast performances. In term of these three criteria the new models outperformed the traditional or classical GARCH model. To drive home this point, these two models were illustrated with Botswana inflation rates data. We observed that the new model (BL-GARCH) outperformed the classical GARCH model. VL - 1 IS - 1 ER -
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