Research ArticleOpen AccessGoogle Scholar indexed
Modeling Exchange Rate Volatility Dynamics of the Great Britain Pound to Ethiopian Birr Using the Semi-Parametric Non-Linear Fuzzy-EGARCH-ANN Model
Mathematics Department, Pan African University (PAU), Nairobi, Kenya
Mathematics Department, Jomo Kenyatta University, Nairobi, Kenya
Mathematics Department, Kenyatta University, Nairobi, Kenya
- 1 Mathematics Department, Pan African University (PAU), Nairobi, Kenya
- 2 Mathematics Department, Jomo Kenyatta University, Nairobi, Kenya
- 3 Mathematics Department, Kenyatta University, Nairobi, Kenya
Journal of Mathematical Finance·Volume 10 (2020)·Pages 598–611·Published 10 October 2020·DOI10.4236/jmf.2020.104035
Copy link · social · email
Abstract
In this paper, a robust analysis of volatility forecasting of the GBP-ETB exchange rate was provided using weekly data spanning the period June 30, 2003-January 24, 2020. To our knowledge, this was the first study that focuses on the GBP-ETB exchange rate using high-frequency data and the Fuzzy-EGARCH-ANN econometric model. The research finds that the best performing model in terms of one-step ahead forecasts based on realized volatility computed from the underlying daily data series is the Fuzzy-EGARCH-ANN(1, 2, 2, 1) with students t -distribution.
KeywordsVolatility ForecastingARCHEGARCHANNSemi-Parametric Non-Linear Fuzzy-EGARCH-ANN Model
- Krugman, P., Obstfeld, M. and Melitz, M. (2012) International Economics, Theory and Policies. 9th Edition, Pearson Addison-Wesley, Boston, 321-495.
- Kamal, Y., Haq, M., Ghani, O. and Khan, M. (2012) Modeling the Exchange Rate Volatility, Using Generalized Autoregressive Conditionally Heteroscedastic (GARCH) Type Model: Evidence from Pakistan. African Journal of Business Management, 6, 2830-2838. https://doi.org/10.5897/AJBM10.1657
- Abdella, S.Z. (2012) Modeling Exchange Rate Volatility Using GARCH Models. Empirical Evidence from Arab Countries. International Journal of Economics and Finance, 4, 216. https://doi.org/10.5539/ijef.v4n3p216
- Ramzan, K. (2012) Modeling and Forecasting Exchange Rate Dynamics in Pakistan Using ARCH Family of Models. Electronic Journal of Applied Statistical Analysis, 5, 15-29.
- Ayalew, S. (2012) Modeling Ethiopian Birr/Dollar Exchange Rate Volatility: Application of GARCH and Asymmetric Models. International Journal of Innovative Research and Development, 1, 160-190.
- de Dieu Ntawihebasenga, J., Mung’atu, J.K. and Mwita, P.N. (2015) Modeling the Volatility of Exchange Rates in Rwandese Markets. American Journal of Theoretical and Applied Statistics, 4, 426-431. https://doi.org/10.11648/j.ajtas.20150406.12
- Dickey, D.A. and Fuller, W.A. (1979) Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association, 79, 427-431. https://doi.org/10.1080/01621459.1979.10482531
- Phillips, P.C. and Perron, P. (1988) Testing for a Unit Root in Time Series Regression. Journal of Biomedicine, 75, 335-346. https://doi.org/10.1093/biomet/75.2.335
- Fufa, D.D. and Zeleke, B.L. (2018) Forecasting the Volatility of Ethiopian Birr/Euro Exchange Rate Using Garch-Type Models. Annals of Data Science, 5, 529-547. https://doi.org/10.1007/s40745-018-0151-6
- Bera, A.K. and Jarque, C.M. (1982) Model Specification Tests: A Simultaneous Approach. Journal of Economics, 20, 59-82. https://doi.org/10.1016/0304-4076(82)90103-8
- Mohammed, G.T., Aduda, J.A. and Kube, A.O. (2020) Improving Forecasts of the EGARCH Model Using Artificial Neural Network and Fuzzy Inference System. Journal of Mathematics, 2020, Article ID: 6871396. https://doi.org/10.1155/2020/6871396
- Dash, R. and Dash, P. (2016) An Evolutionary Hybrid Fuzzy Computationally Efficient EGARCH Model for Volatility Prediction. Applied Soft Computing, 45, 40-60. https://doi.org/10.1016/j.asoc.2016.04.014