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Predicting Exchange Rates Volatility Using Hybrid ARIMA-GARCH Model: A Comparative Analysis
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Author(s): Bruno Dinga (The University of Bamenda, Cameroon), Jimbo Henri Claver (Samarkand International University of Technology, Uzbekistan), Cletus Kwa Kum (The University of Bamenda, Cameroon)and Shu Felix Che (The University of Bamenda, Cameroon)
Copyright: 2025
Pages: 24
Source title:
Data Analytics and AI for Quantitative Risk Assessment and Financial Computation
Source Author(s)/Editor(s): Mohammad Gouse Galety (Samarkand International University of Technology, Uzbekistan), Jimbo Henri Claver (Samarkand Interntional University of Technology, Uzbekistan), A. V. Sriharsha (Mohan Babu University, India), Narasimha Rao Vajjhala (University of New York Tirana, Tirana, Albania)and Arul Kumar Natarajan (Samarkand International University of Technology, Uzbekistan)
DOI: 10.4018/979-8-3693-6215-0.ch005
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Abstract
For the last few decades, the Autoregressive Integrated Moving Average (ARIMA) model has been a popular linear model in predicting time series. Recent research has shown that the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model can be a promising alternative to the traditional linear models. A time series can be considered to comprise a linear and non-linear part. This chapter aims to highlight in a relevant way the need to properly define the dynamics of the linear and non-linear parts of the time series as far as the time series prediction is concerned. Using the GARCH model and the combined ARIMA-GARCH model in the analysis and prediction of exchange rates, this study shows that the combined ARIMA-GARCH-T model, which defines the dynamics of the linear and non-linear parts of the exchange rate time series, has a higher predictive power when compared to the GARCH-T model.
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