Modeling and forecasting exchange rate volatility in Bangladesh using GARCH models: a comparison based on normal and Student’s t-error distribution

S. M. Abdullah (), Salina Siddiqua (), Muhammad Shahadat Hossain Siddiquee () and Nazmul Hossain ()
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S. M. Abdullah: University of Dhaka
Salina Siddiqua: University of Dhaka
Muhammad Shahadat Hossain Siddiquee: University of Dhaka
Nazmul Hossain: University of Dhaka

Financial Innovation, 2017, vol. 3, issue 1, 1-19

Abstract: Abstract Background Modeling exchange rate volatility has remained crucially important because of its diverse implications. This study aimed to address the issue of error distribution assumption in modeling and forecasting exchange rate volatility between the Bangladeshi taka (BDT) and the US dollar ($). Methods Using daily exchange rates for 7 years (January 1, 2008, to April 30, 2015), this study attempted to model dynamics following generalized autoregressive conditional heteroscedastic (GARCH), asymmetric power ARCH (APARCH), exponential generalized autoregressive conditional heteroscedstic (EGARCH), threshold generalized autoregressive conditional heteroscedstic (TGARCH), and integrated generalized autoregressive conditional heteroscedstic (IGARCH) processes under both normal and Student’s t-distribution assumptions for errors. Results and Conclusions It was found that, in contrast with the normal distribution, the application of Student’s t-distribution for errors helped the models satisfy the diagnostic tests and show improved forecasting accuracy. With such error distribution for out-of-sample volatility forecasting, AR(2)–GARCH(1, 1) is considered the best.

Keywords: Exchange rate; Volatility; ARCH; GARCH; Student’s t; Error distribution; C52; C580; E44; E47 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (14)

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DOI: 10.1186/s40854-017-0071-z

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