Forecasting the BDT/USD Exchange Rate Using Autoregressive Model

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Md. Zahangir Alam
Md. Zahangir Alam
1 International Islamic University Chittagong, Dhaka Campus, Bangladesh.

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GJMBR Volume 12 Issue B19

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The key motivation of this study is to examine the application of autoregressive model for forecasting and trading the BDT/USD exchange rates from July 03, 2006 to April 30, 2010 as in-sample and May 01, 2010 to July 04, 2011 as out of sample data set. AR and ARMA models are benchmarked with a naΓ―ve strategy model. The major findings of this study is that in case of in-sample data set, the ARMA model, whereas in case of out-of-sample data set, both the ARMA and AR models jointly outperform other models for forecasting the BDT/USD exchange rate respectively in the context of statistical performance measures. As per trading performance, both the ARMA and naive strategy models outperform all other models in case of in-sample data set. On the other hand, both the AR and naive strategy models do better than all other models in case of out-of-sample data sets as per trading performance.

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No external funding was declared for this work.

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The authors declare no conflict of interest.

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No ethics committee approval was required for this article type.

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Not applicable for this article.

Md. Zahangir Alam. 1970. \u201cForecasting the BDT/USD Exchange Rate Using Autoregressive Model\u201d. Global Journal of Management and Business Research - B: Economic & Commerce GJMBR-B Volume 12 (GJMBR Volume 12 Issue B19): .

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GJMBR Volume 12 Issue B19
Pg. 85- 96
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Crossref Journal DOI 10.17406/GJMBR

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e-ISSN 2249-4588

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The key motivation of this study is to examine the application of autoregressive model for forecasting and trading the BDT/USD exchange rates from July 03, 2006 to April 30, 2010 as in-sample and May 01, 2010 to July 04, 2011 as out of sample data set. AR and ARMA models are benchmarked with a naΓ―ve strategy model. The major findings of this study is that in case of in-sample data set, the ARMA model, whereas in case of out-of-sample data set, both the ARMA and AR models jointly outperform other models for forecasting the BDT/USD exchange rate respectively in the context of statistical performance measures. As per trading performance, both the ARMA and naive strategy models outperform all other models in case of in-sample data set. On the other hand, both the AR and naive strategy models do better than all other models in case of out-of-sample data sets as per trading performance.

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Forecasting the BDT/USD Exchange Rate Using Autoregressive Model

Md. Zahangir Alam
Md. Zahangir Alam International Islamic University Chittagong, Dhaka Campus, Bangladesh.

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