14

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

...............

Department of Business Economics

School of Economics, Bangkok University

E-mail: channarong.c@bu.ac.th

ARIMA . . 55

. . 55

9-

ARIMA (1, , ) (1, 1, )

. . 55

-

15

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

16

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(The urchasing ower arity Theory: )

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(The Absolute

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(The Relative

urchasing ower arity)

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1

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= ih-i

f

e , e1

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ih, i

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(Time Series Data)

( 55 )

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eural etworks

e1-ee

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Autoregressive Integrated Moving

Average (ARIMA) Generalized

AutoRegressive Conditional Heteroskedasticity

(GARCH)

Mean Absolute ercentage Error (MA E)

ARIMA( , , )

MA E .1

ARIMA ( , , ) with GARCH-M (1,1)

GARCH-M

MA E .1 5

eural etworks

1 hidden layer

MA E . 9

MA E

ARIMA with GARCH-M

ARIMA eural etworks

( 55 )

5 9 1

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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

20

(ccc)

Rout, et al. ( 1 ) “Forecasting of

currency exchange rates using adaptive ARMA

model with differential evolution based training”

1-1-19 1-1 - 5, 1-1-19 1 1-1- 5

1-1-19 1 1-1- 5

ARMA

Mammadova ( 1 ) “Forecasting

exchange rates using ARMA and neural network

model” Brazilan

Real US Dollar

1999 1

ARMA

eural etwork Baharumshah, and Sen

( ) “The predictability of the

ASEA -5 exchange rates”

5

1 19 1

1999 Autoregressive

Integrated Moving Average (ARIMA) (p, d, q)

ARMA

Forward-Backward Least-Mean-Square

(FBLMS)

eural

etwork

ARMA ARIMA

-5

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(

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- (Box- enkins)

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(Box, enkins, and Reinsel, 199 )

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(Stationary)

(Autocorrelation

function: ACF) t

( artial autocorrelation function: ACF)

(Yt)

.

1

ACF ACF

. ARIMA (p, d, q) ( ,

S, )

ACF ACF

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ARIMA (p, d, q) ( , S, ) (Seasonal

Autoregressive Integrated Moving Average)

.

ARIMA

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ACF ACF

ARIMA

ARIMA (1, ,1) (1, 1, 1)

ARIMA (1, , ) (1, 1, )

23

ACF ACF

24

ARIMA (1, , ) (1, 1, )

AR(1) . SAR(1)

- .5 . 5 R-squared

. MA E 1.5 5

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ARIMA (1, , ) (1, 1, )

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ACF ACF Box- enkins

(THB/USD)

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( ) .95, .5 , . , 1.

1. THB/USD

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ARIMA (1, , ) (1, 1, )

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Yt-1 t-1

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ARIMA (p, i, q) ( , S, )

ARIMA (1, , ) (1, 1, )

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55

ARIMA

(Ex-post Forecast)

Baharumshah, A. and Sen, L. .

nline]. Available: http://econwpa.

repec.org/eps/if/papers/ / .pdf

Bank of Thailand. 15.

nline]. Available: http://www .bot.or.th/

statistics/B TWEBSTATaspx reportID=

1 language=TH (in Thai).

. 55 .

]. :

h t tp : / /www .bo t . o r . t h / s ta t i s t i cs /

B TWEBSTATaspx report ID=1

language=TH

Box, G.E. ., enkins, G.M., and Reinsel, G.C.

199 . 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.”

, : 1 -11 . (in Thai).

28

. 55 . “

:

-

.”

, : 1 -11 .

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).

. 55 . “

.”

.

Mammadova, G. 1 .

Master’s Thesis, Department of

Economics, Western Illinois University.

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Affecting the Short-term Movement of the

Thai Baht vs the US Dollar.”

, 1: 1-1 . (in Thai).

. 55 . “

.”

, 1:

1-1 .

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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.”

, : 115-1 . (in Thai).

, . 555. “

ARIMA GARCH.”

, :

115-1 .

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and Gold rice olatilities of Thailand Using

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. 55 . “

.”

.

29

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.