Thai Baht and Chinese Yuan Exchange Rate Forecasting Models: ARIMA and SMA-ARIMA Comparison

This study evaluates the effectiveness of classical Autoregressive Integrated Moving Average (ARIMA) models and kth Simple Moving Average - ARIMA (kth SMA-ARIMA) models in forecasting the exchange rate between the Thai Baht (THB) and the Chinese Yuan (CNY). The analysis uses a dataset of historical monthly exchange rates from January 2011 to November 2022, covering 143 months. The dataset is divided into two segments: the initial 127 months are used as the training dataset for model development, while the subsequent 16 months serve as the testing dataset to evaluate forecast accuracy. The Akaike Information Criterion (AIC) is the decision criterion for model selection during the development phase. The forecasting models' effectiveness is subsequently assessed on the testing dataset using two statistical measures: the Mean Absolute Percentage Error (MAPE) and the Root Mean Square Error (RMSE). The findings indicate that the classical ARIMA (0,1,1) model outperforms the kth SMA-ARIMA models in this study, exhibiting the lowest RMSE and MAPE of 0.1702 and 2.6644, respectively. Additionally, a focused comparison of the kth SMA-ARIMA models for k = 2, 3, and 4 reveals that the 2nd SMA-ARIMA (0,1,2) model demonstrates superior performance compared to the 3rd and 4th SMA-ARIMA models. This superiority is reflected in their respective RMSE values of 0.3202, 0.5146, and 0.6339, and corresponding MAPE values of 5.3533, 8.7531, and 10.4949. These results provide valuable insights for decision-makers in the financial sector, enhancing investment strategy formulation based on anticipated currency movements.