Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/176461
Authors: 
Abdullah, S. M.
Siddiqua, Salina
Siddiquee, Muhammad Shahadat Hossain
Hossain, Nazmul
Year of Publication: 
2017
Citation: 
[Journal:] Financial Innovation [ISSN:] 2199-4730 [Volume:] 3 [Year:] 2017 [Issue:] 18 [Pages:] 1-19
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.
Subjects: 
Exchange rate
Volatility
ARCH
GARCH
Student&#x2019
s t
Error distribution
JEL: 
C52
C58
E44
E47
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by/4.0/
Document Type: 
Article
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