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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

has been cited by the following article:

• TITLE: Modeling Exchange Rate Volatility Dynamics of the Great Britain Pound to Ethiopian Birr Using the Semi-Parametric Non-Linear Fuzzy-EGARCH-ANN Model

  AUTHORS: Geleta T. Mohammed, Jane A. Aduda, Ananda O. Kube

  KEYWORDS: Volatility Forecasting, ARCH, EGARCH, ANN, Semi-Parametric Non-Linear Fuzzy-EGARCH-ANN Model

  JOURNAL NAME: Journal of Mathematical Finance, Vol.10 No.4, October 23, 2020

  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.