Forecasting Exchange Rate for USD/CNY : A Box Jenkins Approach with Auto Regressive Integrated Moving Average (ARIMA) Modeling

Whitney Karundeng, Srave Chrisdes Antoro

Abstract


Currency exchange rates have an important impact on the economy and finance fields. Fluctuation between domestic and foreign exchange rate commonly happens because of certain things. Recession, inflations, export and import activities, political stability, and government debt can be certain reasons fluctuations could happen. In this study, Auto Regressive Integrated Moving Average (ARIMA) model is applied to predict exchange rates. The time series data used is about the percentage exchange rate of the Chinese Yuan Renminbi to the US Dollar provided weekly from February 2022 - February 2023 with a total of 54 data. The result shows that the ARIMA model (2,2,1) was the most accurate model among other ARIMAs for examining our data. The percentage of (Mean Absolute Percentage Error) MAPE ARIMA (2,2,1) is 1.48%

Keywords


Forecasting; Exchange rate; ARIMA; Box Jenkins; Stationarity; USD; Chinese Yuan; US Dollar

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