TITLE:
Bootstrap Approaches to Autoregressive Model on Exchange Rates Currency
AUTHORS:
Muhamad Safiih Lola, Anthea David, Nurul Hila Zainuddin
KEYWORDS:
Autoregressive Model, Outliers, Bootstrap, Robust
JOURNAL NAME:
Open Journal of Statistics,
Vol.6 No.6,
November
17,
2016
ABSTRACT: The use of historical data is important in
making the predictions, for instance in the exchange rate. However, in the
construction of a model, extreme data or dirtiness of data is inevitable. In
this study, AR model is used with the exchange rate historical data (January
2007 until December 2007) for USD/MYR and is divided into 1-, 3- and
6-horizontal months respectively. Since the presence of extreme data will
affect the accuracy of the results obtained in a prediction. Therefore, to
obtain a more accurate prediction results, the bootstrap approach was
implemented by hybrid with AR model coins as the Bootstrap Autoregressive model
(BAR). The effectiveness of the proposed model is investigated by comparing the
existing and the proposed model through the statistical performance methods
which are RMSE, MAE and MAD. The comparison involves 1%, 5% and 10% for each
horizontal month. The results showed that the BAR model performed better than
the AR model in terms of sensitivity to extreme data, the accuracy of forecasting
models, efficiency and predictability of the model prediction. In conclusion,
bootstrap method can alleviate the sensitivity of the model to the extreme
data, thereby improving the accuracy of forecasting model which also have high
prediction efficiency and that can increase the predictability of the model.