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ARPN Journal of Science and Technology >> Volume 7, Issue 1, January 2017

ARPN Journal of Science and Technology

Forecasting Daily Bangladeshi Exchange Rate Series based on Markov Model, Neuro Fuzzy Model and Conditional Heteroskedastic Model

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Author Shipra Banik, Mohammed Anwer, A.F.M. Khodadad Khan
ISSN 2225-7217
On Pages 186-192
Volume No. 3
Issue No. 2
Issue Date March 01, 2013
Publishing Date March 01, 2013
Keywords Forecasting, Markov model, non-linearity, artificial neural network models, fuzzy logic, heteroscedasticity, time series model.


Prediction of exchange rate is very important for many international agents e.g. investors, money managers, investment banks, funds makers and others. We forecasted the daily Bangladeshi exchange rate series for the period of January 1992 to March 2009 using popular non-linear forecasting models, namely Markov switching autoregressive (MS_AR) model, fuzzy extension of artificial neural network model (ANFIS) and generalized autoregressive conditional heteroscedastic (GARCH) model. Our target is to investigate whether selected models can serve as useful forecasting models to find volatile and non-linear behaviours of the considered series. By most commonly used statistical measures: mean absolute percentage error, root mean square error and coefficient of determination, we found that ANFIS is a superior predictor than other two selected predictors. We believe findings of this paper will be helpful to make a wide range of policies for multinational companies who are involved with various international business activities.

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