Forecasting Thai Baht vs U.S. Dollar Rates Using the ARIMA Model

Transcription

1 Forecasting Thai Baht vs U.S. Dollar Rates Using the ARIMA Model. Department of Business Economics School of Economics, Bangkok University ARIMA ARIMA (1,, ) (1, 1, )

2 Abstract Since Thailand adjusted to implement a floating exchange rate regime, the exchange rate of the Thai baht against the U.S. dollar has been changing all the time. This has affected loss or gain profits for international business transactions as a result of foreign exchange rate fluctuations. This study aims to create a model to estimate and forecast the exchange rate of the Thai baht against the U.S. dollar based on analysis of the time series using the ARIMA model and a data collection from anuary 11 to ovember 1. The findings reveal that during the study period the THB/USD exchange rate fell between THB 9- / USD. The findings also indicate that the appropriate ARIMA model for forecasting the exchange rate of THB/USD is (1,, ) (1, 1, ). When using this model to predict the exchange rate of THB/USD in the second half of the year 15, it turns out that the exchange rate tends to be stronger, reaching THB - / USD. This currency movement is consistent with the fact that the U.S. economy is slowly recovering. Therefore, business transactions dealing with USD should be done with extreme care. Traders, especially importers, are recommended to have risk assessment and preventive measures because the U.S. currency is likely to become stronger. THB/USD Exchange Rate, ARIMA Model, Forecasting 15

3 (Managed Float) :, 55 16

4 (, 55: 11-11;, 555: 115-1) (The urchasing ower arity Theory: ) 17

5 (The Absolute urchasing ower arity) (The Relative urchasing ower arity) 1. 1 S i i / i S i 1 i i. e 1 -e e = i h -i f e, e 1 1 i h, i f (Time Series Data) (55) eural etworks 18

6 Autoregressive Integrated Moving Average (ARIMA) Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) Mean Absolute ercentage Error (MAE) ARIMA(,,) MAE.1 ARIMA (,,) with GARCH-M (1,1) GARCH-M MAE.15 eural etworks 1 hidden layer MAE.9 MAE ARIMA with GARCH-M ARIMA eural etworks (55) ,19 1) Augmented Dickey-Fuller Test (ADF) (ARMA (p,q)) ) ector Autoregressive Moving Average-GARCH (ARMA-GARCH) ector Autoregressive Moving Average Asymmetric GARCH (ARMA-AGARCH) Constant Conditional Correlation (CCC) ARMA AR() MA() ARMA AR(1) MA(1) ARMA-AGARCH (1,1) CCC ARMA- AGARCH 19

7 (ccc) Rout, et al. (1) Forecasting of currency exchange rates using adaptive ARMA model with differential evolution based training , ARMA Mammadova (1) Forecasting exchange rates using ARMA and neural network model Brazilan Real US Dollar ARMA eural etwork Baharumshah, and Sen () The predictability of the ASEA-5 exchange rates Autoregressive Integrated Moving Average (ARIMA) (p, d, q) ARMA Forward-Backward Least-Mean-Square (FBLMS) eural etwork ARMA ARIMA (, 55: 1-1) ARIMA - (Box-enkins)

8 (t) - (Box, enkins, and Reinsel, 199) Y t (Y t ) Y (Y t-1, Y t--,..) Y t Y t-1, Y t-.. Y t1, Y t, (Stationary) (Autocorrelation function: ACF) t (artial autocorrelation function: ACF) (Yt). 1 ACF ACF. ARIMA (p, d, q) (, S, ) ACF ACF. 5. ARIMA (p, d, q) (, S, ) (Seasonal Autoregressive Integrated Moving Average). ARIMA (t, Y) t Y 9-21

9 ACF ACF ARIMA ARIMA (1,,1) (1, 1, 1) ARIMA (1,, ) (1, 1, ) 22

10 ACF ACF 23

11 ARIMA (1,, ) (1, 1, ) ARIMA (1,, ) (1, 1, ) 1. AR(1). SAR(1) R-squared. MAE

12 . Kolmogorov.5 ormal Distribution. ACF ACF ( 5) lag White oise ACF ACF Box-enkins (THB/USD)

13 ().95,.5,., THB/USD 55 ().55,.5,.5,.9,.,.,.. THB/USD ARIMA (1,, ) (1, 1, ) t 1 Y t-1 t-1-1 Y t-2 1 Y t Y t-13-1 Y t Y t-14 t t-1 t y t-12 t y t-14 t. AR(1). AR, Seasonal(1) -.5 t (THB/USD)

14 ARIMA (p, i, q) (, S, ) ARIMA (1,, ) (1, 1, ) ARIMA (Ex-post Forecast) Baharumshah, A. and Sen, L.. nline]. Available: repec.org/eps/if/papers//.pdf Bank of Thailand. 15. nline]. Available: statistics/btwebstataspxreportid= 1language=TH (in Thai) ]. : BTWEBSTATaspxreportID=1 language=th Box, G.E.., enkins, G.M., and Reinsel, G.C rd. ed. Englewood Cliffs, : rentice-hall. Hatchavanich, D. 1. A Comparison of Forecasting Models for the Monthly Consumer rice Index: Box-enkins and Exponential Smoothing Models., : (in Thai). 27

15 . 55. : -., : ansod. A.. Accuracy Comparison in Foreign Exchange Rate Forecasting Between eural etworks and ARIMA GARCH-M Models. Master s Thesis, Department of Economics, Chiang Mai University. (in Thai) Mammadova, G. 1. Master s Thesis, Department of Economics, Western Illinois University. Rattanapongpinyo. T. 1. A Study of Factors Affecting the Short-term Movement of the Thai Baht vs the US Dollar., 1: 1-1. (in Thai) , 1: 1-1. Rout, M., et al. 1. Forecasting of Currency Exchange Rates Using and Adaptive ARMA Model with Differential Evolution Based Training., 1: -1. Saothayanun, L., et al. 1. A Comparison of the Forecasts for Rubber rices Using ARIMA and GARCH Models., : (in Thai)., ARIMA GARCH., : Sinchaikit, S. 11. Modeling of Exchange Rate and Gold rice olatilities of Thailand Using Bivariate GARCH. Master s Thesis, Department of Economics, Chiang Mai University. (in Thai)

16 Channarong Chaiphat received his Master of Economics from Kasetsart University, Thailand. He is currently an assistant professor at the School of Economics, Bangkok University. His main interest is in International Monetary Economics. 29