Determinants of foreign exchange rate: case of Vietnamese Dong and Japanese Yen

UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM INSTITUTE OF SOCIAL STUDIES THE HAGUE THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS DETERMINANTS OF FOREIGN EXCHANGE RATE: CASE OF VIETNAMESE DONG AND JAPANESE YEN BY Mr TRAN VUONG TU MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, MAY 2013 UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM INSTITUTE OF SOCIAL STUDIES THE HAGUE THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS DETERMINANTS OF FOREIGN EXCHANGE RATE: CASE OF VIETNAMESE DONG AND JAPANESE YEN A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By Mr TRAN VUONG TU Academic Supervisor: PhD NGUYEN HOANG BAO HO CHI MINH CITY, MAY 2013 ACKNOWLEDGEMENT This thesis was written at the University of Economics Ho Chi Minh City In addition, it was completed in October 2013 During the process of writing, the paper has gained a lot of experience in writing a thesis and in the area of foreign exchange rate analysis During the three months of writing this thesis, several persons have contributed in the different ways to the quality of this thesis and the paper would like to take this opportunity to thank them Firstly, the paper would like to thank our supervisor PhD Nguyen Hoang Bao for all the help, guidance, and support The paper would also like to express gratitude to all professors of the Vietnam-Netherlands Program for the Master in Development Economics and the classmates who offer to us some useful suggestions Finally, we express special thank to our families and partners for their love and support ABSTRACT Exchange rate not only plays a very important role in the economic policy of the government of Vietnam in the process of integration into the world economy, but also effects many exporters, importers, foreign investors, and commercial banks in the international transaction Japanese economy plays as important as having mainly economic relations with Vietnamese economy in the export-import trade, foreign direct investment (FDI) capital, official development assistance (ODA), etc However, Vietnam government applies the floating exchange rate policy between Vietnamese Dong and the Japanese Yen Therefore, the fluctuations of Vietnamese-Japanese exchange rate might great impact on the trade and investment The exporters and importers of two countries, Japanese investors, the commercial bankers that having international settlement with Japanese Yen, are in need of defending the exchange rate risk volatility of the exchange rate pairs Our study enhance on analyzing and predicting the fluctuations of Vietnamese-Japanese exchange rate The main research question identifies (1) Which Vietnamese and Japanese macroeconomics variables determine the VND/JPY exchange rate; (2) What the role of the Japanese Yen plays in the economic relationship between Vietnam and Japan and (3) Which performance of the multiple regression model and the autoregressive integrated moving average model are in predicting the VND/JPY exchange rate Methodology focuses on the multiple regression model to define the determinants Moreover, our study test the reliability in the prediction between multiple regression model and auto-regressive integrated moving average model to examine the VND/JPY exchange rate data Hence, auto-regressive integrated moving average model plays better forecasting performance Key Words: VND/JPY exchange rate, multiple regression model, auto-regressive integrated moving average (ARIMA), Vietnamese Dong, Japanese Yen, Vietnam, Japan TABLE OF CONTENTS Table of contents List of tables List of figures List of abbreviations Chapter one: Introduction 1.1 Background of study .5 1.2 Research question 1.3 Research objective 1.4 The outline of paper Chapter two: Literature review .9 2.1 Theoretical framework 2.2 Empirical Studies 14 Chapter three: Methodology 17 3.1 Data .17 3.2 The fundamental regression model .18 3.3 Box-Jenkins’ auto-regressive integrated moving average model (ARIMA) 19 Chapter four: The impact of the Japanese Yen in the economic relationship between Vietnam and Japan 23 4.1 Overview of the Vietnamese foreign exchange policy 23 4.2 Overview of the Japanese foreign exchange policy 25 4.3 The impact of the Japanese Yen in the trade, investment, and finance between Japan and Vietnam 27 Chapter five: Results: Descriptive data, multiple regression and ARIMA 32 5.1 Descriptive statistics .32 5.2 The results and summary of findings 34 5.3 Forecasting performance .38 Chapter six: Conclusions .41 6.1 Summary of study 41 6.2 Policy implication 42 6.3 Limitation of our study and suggestion for further research 42 References 44 Appendix A 50 Appendix B 52 Appendix C 60 Appendix D .70 LIST OF TABLES Tables 2.1 Description of economic indicators 12 Tables 2.2 Empirical Studies 14 Tables 3.1 Variable sources 17 Tables 3.2 List of variables 18 Tables 3.3 The autocorrelation function (ACF) and the partial autocorrelation function (PACF) patterns summary 20 Tables 4.1 Global foreign exchange reserves .26 Tables 4.2 History of the Japan's interventions in the foreign exchange rate 26 Tables 5.1 Description of the variables .32 Tables 5.2 Correlation test and Anova F-test 33 Tables 5.3 The result regression model 34 Tables 5.4 Wald Test 35 Tables 5.5 Unit Root Test 36 Tables 5.6 ARIMA statistical results 37 Tables 5.7 The VND/JPY forecasting performance 38 Tables 5.8 Testing of forecasting ARIMA 39 Tables 5.9 The advantages and disadvantages in the multiple regression and Autoregressive integrated moving average model (ARIMA) .40 LIST OF FIGURES Figure 3.1 Box Jenkins Methodology for ARIMA modeling .19 Figure 4.1 Value of trade balance Vietnam-Japan 28 Figure 5.1 The plot of the monthly VND/JPY exchange rate 36 LIST OF ABBREVIATIONS S.No 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 abbreviation AF ARIMA BOJ CBT CIEM FA FDI GDP GSO IFE IPI JPY JVEPA LCO (WTI oil) MFAJ MFJ MPIV MR NAEC ODA PAF RBA SBV TA TB USD VGDC VND Vietcombank VFA WTO Description Autocorrelation function Auto-regressive integrated moving average Bank of Japan Chicago board of trade Central institute for economic management Fundamental analysis Foreign direct investment Gross domestic product General statistics office of Vietnam International Fisher effect industrial production index Japanese Yen Japan-Vietnam economic partnership agreements Light crude oil (West Texas intermediate oil) Ministry of Foreign Affairs of Japan Ministry of Financial of Japan Ministry of Planning and Investment of Vietnam Multiple regression National Assembly's economic Committee Official development assistance Partial autocorrelation function Royal bank of Australia State bank of Vietnam Technical analysis Trade balance U.S Dollar Vietnam general department of customs Vietnamese Dong Joint stock commercial bank for foreign trade of Vietnam Vietnam food association World trade organization CHAPTER ONE: INTRODUCTION This paper presents the research projects including studies of users, target research to identify the factors that influence the exchange rate between the Vietnamese Dong and Japanese Yen Accordingly, fluctuations in foreign exchange rate have great impact on Vietnamese government, the exporters, importers, commercial banks, and Japanese investors In addition, they have a need for the research prediction on foreign exchange rate Therefore, our research focuses on analyzing and predicting the fluctuations in VND/JPY exchange rate by multiple regression and auto-regressive integrated moving average (ARIMA) Our finding is what determinants of VND/JPY exchange rate in Vietnam 1.1 Background of study Vietnam economy increasingly integrated into the world economy in term of trade and investment Therefore, exchange rate plays a very important role in the economic policy of the Vietnamese government Moreover, many exporters, importers, foreign investors, and commercial banks, that make the international transactions, are impacted by the fluctuations in foreign exchange rate Indeed, many countries fall into economic hardship due to unstable exchange rate, such as trade deficit, high inflation, increasingly foreign debts, etc Therefore, the exchange rate has attracted special attention to the economists, politicians for the study and research In addition, the exchange rate has become an important topic, which is discussed and analyzed on over the world Many researches have done in order to predict and analyze the fluctuations in foreign exchange rate An opening economy is towards the integration with the world economy as well as Vietnamese economy According to the report on April 2013, Vietnamese Ministry of Planning and Investment, issued "Comprehensive evaluation of Vietnam’s socioeconomic performance five years after the accession to the World trade organization" report1 This report has identified the economic policy focused on foreign trade of Vietnam is following the trend of multilateral development cooperation The International trade of Vietnam and the rest of world become very exciting However, the Vietnam’s exchange rate policy used to concentrate on implementing the pegged exchange rate policy between Vietnamese Dong and U.S Dollar, and keep floating alongside most of other exchange rates It creates some difficult, risky factors for exporters, importers, commercial banks, and foreign investors during the international payment process for non-dollar currencies such as the Japanese Yen, Euro, and Australian Dollar etc Indeed, the General statistics office of Vietnam on the international trade in Vietnam in 2012 said that Vietnam has many international economies Beside the United State of America as Vietnam’s largest trade partner with two-way trade turnover reached US$ 27.6 billion, Vietnam still has many key trading partners such as Japan (approximately U.S Dollar 24.6 billion, 11.1% of Vietnamese total trade)2, South China (U.S Dollar 19.5 billion) and the Association of Southeast Asian Nations (ASEAN) Japan, in particular, is a country having highly economic relations with Vietnam in many fields including export-import, foreign direct investment capital, Official development assistance, and etc According to the Department of Foreign Affairs under the Ministry of Planning and Investment, in the years 2012-2013, Japan continues to be the largest donor of official development assistance (ODA) for Vietnam with 40%3 of total official development assistance (ODA) commitments to Vietnam According to the Foreign investment agency under the Ministry of Planning and Investment, in 2012, Japan was the biggest foreign direct investment investor in Vietnam, accounted for 34.2%4 of total investment in Vietnam Thereby, indicating ‘Comprehensive evaluation of Vietnam’s socio-economic performance five years after the accession to the World Trade Organization’ 2013, report 2013, Central institute for economic management, Vietnamese Ministry of Planning and Investment General statistics office of Vietnam 2013, Vietnam Export-Import report 2011-2012, Vietnam Department of foreign affairs 2013, Official development assistance report 2011-2012, Ministry of Planning and Investment in Vietnam Foreign investment agency 2013, Foreign direct investment report 2011-2012, Ministry of Planning and Investment in Vietnam 10 XRATE = C(1) + C(2)*IIPVN + C(3)*IRVN + C(4)*CPIVN + C(5)*TRADEVN + C(6)*RICEVN + C(7)*IIPJP + C(8)*IRJP + C(9)*CPIJP + C(10)*TRADEJP + C(11)*OIL + C(12)*XRATE(-1) + [MA(10)=C(13),BACKCAST=2006M02,ESTSMPL="2006M02 2012M12"] Substituted Coefficients: ========================= XRATE = -30.135374111 - 0.0331329298633*IIPVN - 0.187394574032*IRVN + 0.102689340839*CPIVN 0.0397720022944*TRADEVN + 10.2760932463*RICEVN + 0.00867569847378*IIPJP - 4.13190031879*IRJP + 0.282297314597*CPIJP + 0.0627461578307*TRADEJP - 15.0574006791*OIL + 0.987537618531*XRATE(-1) + [MA(10)=0.342905262593,BACKCAST=2006M02,ESTSMPL="2006M02 2012M12"] Restricted Model included XRATE(-1) and MA(10) Estimation Command: ========================= LS(DERIV=AA) XRATE C OIL XRATE(-1) MA(10) Estimation Equation: ========================= XRATE = C(1) + C(2)*OIL + C(3)*XRATE(-1) + [MA(10)=C(4),BACKCAST=2006M02,ESTSMPL="2006M02 2012M12"] Substituted Coefficients: ========================= XRATE = 2.99412900742 12.083799246*OIL + 0.991451908091*XRATE(-1) [MA(10)=0.265782214267,BACKCAST=2006M02,ESTSMPL="2006M02 2012M12"] + 61 Breusch-Godfrey Serial Correlation LM Test: 62 Wald Test: 63 APPENDIX C In an ARIMA model, the paper does not have a priori for forecasting model before model identification takes place ARIMA helps us to choose a “right model” to best fit the time series Put it in a flow chart: Demonstration to find "right ARIMA model (p, d, q)" to fit the time series through trial and error The resulting graphs are: 64 The PACFs are used to determine the order of the AR component, and ACFs to decide the order of the MA component ARIMA(p,d,q) – p is the order of the AR component; d is the level# of difference; q is the order of the MA component From the above graphs, you can see that the time series is likely to have random walk pattern, which random walk up and down in the line graph Also, in correlogram, the ACFs are suffered from linear decline and there is only one significant spike for PACFs The graph of correlogram suggests that ARIMA(1, 0, 0) may be an appropriate model Then, the paper takes the first-difference of "xrate" to see whether the time series becomes stationary before further finding AR(p) and MA(q) To see whether first difference can get level-stationary time series or not, the paperneed to generate it "Dxrate=d(xrate)" Then, you will use it to draw a line graph and also get a correlogram graph 65 Unit root test on Dxrate 66 Result from unit root test show that the Augmented Dickey-Fuller test statistic = 6.96358 is outside the range of the test critical values at 1%, 5% and 10%, hence the paper reject the null hypothesis that D(xrate) has a unit root or in other words, D(xrate) is stationary Now, the first-difference series "Dxrate" becomes stationary as showing in line graph and is white noise as shown no significant patterns in the graph of correlgram And the unit root test also confirms the first-difference becomes stationary The strong evident support that the ARIMA(0,1,0) is suitable for the time series Then, the paper can construct the ARIMA model as following steps: 67 Seems that AR(1) still exist The paper can another test LAGRANGE MULTIPLIER TEST (LM TEST) to confirm: Lags = means the paper test whether both AR(1) and AR(2) =0 68 From the result, P-value = 0.0844 > 0.05 >>> The paper fail to reject the H0 that both AR(1) and AR(2) = So the paper continue test with lags = (test whether AR(1) = 0) 69 The test reject H0 that AR(1) = >>> AR(1) does exist Now, the paper turn to estimation equation again to run regression with both AR(1) and AR(2) included: 70 Now, since there is no significant spikes of ACFs and PACFs, and the LM test strongly reject the null hypothesis, it means that the resduals of this selected ARIMA model 71 are white noise, so that there is no other significant patterns left in the time series, then the paper can stop at here and don't need to further consider another AR(p) and MA(q) The ARIMA(2,0,0) is a good model with white noise and without AR The criterions to judge for the best model are as follows: - Relatively small of BIC (Schwarz criterion which is measured by nLog(SEE)+kLog(n)) - Relatively small of SEE - Relatively high adjust R2 - Q- statistics and correlogram show that there is no significant pattern left in the ACFs and PACFs of the residuals, it means the residuals of the selected model are white noise 72 The ARIMA (1, 0, 1) 73 APPENDIX D The forecasting performance results of fundamental regression The forecasting performance results of ARIMA (2,0,0) model 74 The forecasting performance results of ARIMA (1,0,1) model 75