Macroeconomic factors affecting Exchange Rate-BRICS Countries

 

Soumya Ranjan Mishra

Department of Finance, Institute of Management, Christ University, Hosur Road, Bangalore-560029, India

 

ABSTRACT:

This paper basically gives us an empirical study of macro variables factors which affects the foreign exchange or currency market. Exchange rate is an imperative part of a country’s economy. The foreign exchange is undergoing substantial changes over the period of time. The research is being directed with a specific end goal to find a connection between macro variables and exchange rate. The direct measure of expectations builds on the information that is contained in data from the foreign exchange market. This research inspects the impact of macro variables on the exchange rate of five different countries i.e. Brazil, Russia, India, China and South Africa. This study uses panel data regression methods to establish a relationship between this variable as well as its impact on currency market. So, here we are taking exchange rate as a dependent variables and independent variables such as GDP, imports, exports, CPI, Industrial Production and Total Reserve. It is significant to highlight that the macroeconomic arrangements must be executed so as to balance out and diminish the trade rates volatilities. Exchange rate also depends upon supply and demand from one country to other countries. For these reasons studying macroeconomic variables their impact on exchange rates are of utmost significance, which also helps to understand what, how and when these variables determine the exchange rates. There are also some other factors which also impact the exchange rate but are not taken into consideration. The research is based upon secondary data which is collected from different sources like World Bank, IMF etc.

 

 

 

INTRODUCTION

Foreign exchange is a market place which is engaged in buying and selling of foreign currency. Each country is involved in international trade and investment of its own currency. The foreign exchange market by its nature is multinational in scope. The leading centers for foreign exchange dealings are in London, New York and Tokyo. An exchange rate provides a gateway to country’s economic stability and that is why it is constantly monitored and analyzed. It influences price stability, firm profitability and nation’s financial strength.

 

 

Today’s scenario, where business trend is changing by day by day and it is not restricted to a particular country. Due to globalization, liberalization and privatization, the management of foreign exchange rate and many macro variables are responsible for the movement of forex. If the exchange rate is poorly managed, it can be disastrous to the economic growth of a country. Understanding the basic concept of exchange is very relevant since transaction inside and outside are affected by exchange rate. It is fundamentally a linkage between remote and foreign market for goods and services. By the help of exchange rate, we can compare price of goods and services as well as assets present in different currencies. The interest of study to take certain factors and predict how they affect the exchange rate. Many researcher have used different methods of exchange rate movements, here panel data regression method is being used in this study. A real understanding mechanism of this study will help the economist of this world. Therefore, it is quite essential to study about macro variables factors which are responsible for variation of exchange rate.

 

The research will contribute to the body of knowledge by reducing the gap in knowledge especially in recent Indian context as recommendations which will be applicable for the investors, MNCs, market players, Government, companies from all sectors, importers and exporters. Thus, research can contribute to the economy and policy making as it identifies the relations and their significance between macroeconomic variables and exchange rate variability. Developments in today’s exchange rate are influencing tomorrow exchange rate level and thus the Central bank loss function. The effect of exchange rate to the macroeconomic variables is a basic factor that policymakers need to consider, beside from determining factors affecting exchange rate movements between nations. In addition to that, this will help them in choosing what issues and task are to be considered, particularly in making the monetary and fiscal policy.

 

LITERATURE REVIEW:

Exchange rate is an imperative part of a country’s economy. There are number of factor which determines the volatility of exchange rate. Import and Export of a country is governed by exchange rate. Various researchers in the past have done on foreign exchange rate fluctuations by using different technique and using various variables. A study in this context was done by Vinod Bhatnagar and Bhavana Singh (2013) have studied about the empirical relationship of countries GDP and Inflation with respect to exchange rate over the period of 1990 to 2010. Linear multivariate regression was used to find out cause and effect relationship among foreign exchange rate, GDP and inflation. In a scatter plot diagram, point was showing the linearity i.e. correlation coefficient will accurately measure the relationship. Ashish and Vidhu Noel (2012) explains behavior of one currency with respect to other currency. To know the relationship between the independent and dependent variable correlation using excel and t-test were done in this research. Katarzyna Twarowska and Magdalena Kąkol (2014) explains that long term exchange rate determination is depends upon the notion of PPP (Purchasing Power Parity) and the short term exchange rate is determined by Uncovered Interest Parity (UIP). The authors classifies the factors affecting exchange rate into two groups based upon long and short term and the other classification in based upon economic and non-economic factors. Raja Sher Ali Khan (2014) author says that the analysis can contribute in the strategy making and execution of the policies like monetary, fiscal and trade policy. ANOVAs and Correlation were the tools to study the effect of macro variables with respect to exchange rate. Dr. Ashok Patel (2014) talks about the graph of currency which has been dramatically changed over the years. Interest Rate Parity is similar concept to PPP which means exchange rate between two nations is equal to the currencies respective purchasing power. It can be measure accuracy of above given prediction models using different measures of forecast accuracy like AARCH and GARCH.Ravi Bhandari (2014) talks about the depreciation of rupee that had lead on 1991 scenario. Descriptive research has been taken into consideration and correlation has been found between factors and exchange rate. The author is taking the consideration of 1991 Indian economic crisis which was due to depreciation of rupee against US dollar. India followed fixed rate currency system with the currency pegged against GBY after independence. Kazi Mohammed Kamal Uddin (2013) paper finds the real swap rate and the macro variables affecting the rate frames a co integrating factor. Augmented Ducky Fuller Test has been done in this model and can be represented by stationary and non-stationary process. Dr. Agustina Tan Cruz (2012) talks about the RID (Real Interest Differential) model assisted by the Keynesian and Chicago price theories. Long term and short term exchange rates were measured by Ordinary least square method. Muhammad Farooq (2014) investigates the experimental connection between exchange rate instability and economic growth has been found while employing error correction technique along with auto-regressive model. An econometrics model named Auto Regressive Distributed model is used to analyze the trend and pattern of exchange rate volatility and its impact on economy. Anita Mirchandni (2013) says that flexible exchange rate is built upon the assumption that the stabilizing behavior of speculators will make exchange rate relatively small compared to fixed rate. Dr. Jain Mathew (2015) gives a monetary as well as asset view of the exchange rate determination. Analytical research using statistical tool i.e. correlation analysis by using SPSS. Ayaz (2012) paper gives us an idea about the reason behind fluctuation of exchange rate which is due to change in demand and supply. Mustafa Sayim (2016) uncovers the pattern of foreign exchange could stimulate the international trade, as well as promote the potential opportunities of investing in the future. Descriptive statistics were used for the variables i.e. mean, std. deviation, skewness and kurtosis. Golan Benita and Beni Lauterbach (2007) has study the instability of the exchange rate between the US dollar and 43 other currency of the world. Statistical and macroeconomic factors also help to clarify exchange rate stability. As per researcher, exchange rates follow GARCH (Generalized Auto Regressive Conditional Heteroscadicity) process. In case of GARCH model, the exchange rate variance is constant and serially correlated. Handan S. Adhikari (2016) explains the reason behind the fluctuation of exchange rate which is due to capital transfer and flow of trade across the world. Data analysis were done in different test like unit root test, lag selection, Johansen co-integration test, Long run co-integration equation, short term relationship. Dr.S.Poornima investigates the impact between the macro variables factor and exchange rate causing fluctuation in the value of rate and its effect for any open economy. According to researcher, the price of the currency is determined by the demand and supply. M.S. Ponnamma (2017) explores the macro economic factors affecting the exchange rate v/s Indian rupee. Different methodology has been used i.e. Augmented Dickey Fuller Test-Unit root, vector auto regression, histogram-normality test, Breusch-Godfrey serial correlation, Breusch-Pagan-Godfrey Heteroskedasticity, stability test, Granger causality test, auto-regressive distributive lag model. Monika kakhani (2012) try to analyze regardless of whether a casual relationship exists between foreign exchange rate and securities exchange. Techniques like granger causality and correlation test to find the relationship between exchange rate and Indian stock market indices. Dr. A. Saravanam (2015) talks about the depreciation of rupee against dollar. The reason for the study is to analyze the development of exchange rate in Indian rupee between Pre-liberalization and Post-Liberalization and furthermore to look at the growth of foreign investments flow in the Indian capital market. Analysis is being done with the statistical tool namely mean, standard deviation, coefficient of variance, CAGR, correlation and paired t test.

 

 

 

 

RESEARCH METHODOLOGY:

Estimation equation:

ER = C(1) + C(2)*CPI + C(3)*EXP01 + C(4)*IMP + C(5)*GDP +

          C(6)*IP + C(7)*TR + [CX=F,PER=F]

Figure 1 Panel data regression equation

According to the regression equation, C (1), C (2) C (7) represents the coefficients of the independent variable. The dependent variable here is Exchange rate and CPI (Consumer Price Index), Exp (Export), Imp (Import), GDP (Gross Domestic Product), IP (Industrial Production) and TR (Total Reserve) are the independent variable. The software used for this research is E-views 9.5 and panel data regression method is used to predict the dependent variable upon independent variables. Secondary data is collected from different sources such World Bank and International monetary fund. The number of observation collected in the research is 40 nos. BRICS countries data has been collected and exchange rate is compared with the standardized currency i.e. with US dollar to provide uniformity in the data. Panel data has been used to do this research and 5 countries taken into consideration. Different codes are given to each country. Time taken for the research is 8 years i.e. from 2009 to 2016.

 

Hypothesis setting:

:-There is a no significant impact of macroeconomic factors influencing the foreign exchange rate of BRICS countries. :-There is a significant impact of macro-economic factors influencing the foreign exchange rate of BRICS countries. Confidence interval taken for analysis is 95%. So, if the p-value is lower than 0.05,  is rejected and thus the variable significantly affects the exchange rate. Otherwise, if the P value is higher than 0.05, then  is accepted thus the variable does not significantly affects the exchange rate.

 

 

Figure 2 Panel data regression and specification

 

Figure 3 Exchange rate forecast

 

 

Figure 4 Residual

 

 

Figure 5 Model based equation

 

ANALYSIS AND INTERPRETATIONS:

As per results of fixed panel regression, it has been seen that, GDP, Import and Total Reserve has an high occurrence of 95.71%, 99.25%, 95.71 with a p-values like 0.0429, 0.0011, 0.0075 respectively These independent variables have high occurrence which suggest that they have a significant impact on dependent variables that is exchange rate here. The other independent variable, which is industrial production, export and CPI do not influence the exchange rate to a significant extent as per test result. The R-squared is at 0.99, which means that the prediction success rate is 99%. For instance, if the model is run 100 times, there are chances of 99times success rate which is quite high and gives a positive response about the data. F-statistics is high at 1168.619.As per F-statistics which means that the average deviation is high and the error tend to be normally distributed, which is very good sign for the model. F-statistic is calculated as average deviation divided by average error. It imparts an inverse relationship with the average error. Hence, higher the F-statistic, lower is the error and the other way round. This implies that the model is sustainable in upcoming future due to the presence of less skewed errors. The value of AIC, SC and HQ should be within the range of 0-10. In Fig 1, AIC, BIC and HQ all are in the range of-1 to-2, which means a less force is needed to reduce or press down the error, which is good for the model, thus indicating that the prediction model is robust. Durbin Watson of 1.58, so it indicates that there is feeble positive autocorrelation in the data. From above conclusion, we can say that the model is robust and sustainable for long run.

 

In fig 2, the forecast numbers of RMSE, MAPE, MAE and Theil are used to evaluate different metrics of errors. Following are the criteria which are used to judge the different errors of forecast. The errors in forecast (RMSE, MAE, MAPE and TIC) are significantly lower than the threshold levels of 1, 1, 5, 1 respectively indicating that the model is sustainable. In fig 2, we can see that, all the four RMSE, MAE, MAPE and Theil Inequality coefficient is meeting the criteria specified above and are passing the test. Hence, since all four measures are passing the test, the model can be accepted.

 

As per fig 3 above, we can see that the actual and fitted lines are completely overlapping each other, which indicates the model will be able to make accurate predictions. Goodness of fit is very accurate and the model is robust. Residuals are very low.

 

CONCLUSION:

From the above research, it can be concluded that this study provided a standardized and reliable approach to measure the variables that are affecting the exchange rate. Regression is a statistical technique which is used to determine the casual effect of exchange rate on macroeconomic variables. Macroeconomic factors such as GDP, CPI, Industrial Production, foreign reserves, import and export are taken into consideration. In this research, 95% confidence interval has been taken. As the p value for the factors i.e. GDP, Total Reserve and Import are less than 0.05, we accept the alternate hypothesis and reject the null hypothesis. As per results in panel data regression, it can be interpreted that GDP, Total Reserve and Import have a significant impact on foreign exchange rate. So, exports, industrial production and CPI do not have a significant impact on foreign exchange rate.

 

LIMITATIONS OF THE STUDY AND SCOPE FOR FUTURE WORK:

In this research, some variables are taken into consideration, inclusion of additional variables are suggested. Non-economic factors like political risk, natural disaster, policy approaches and psychological factors can be taken in this research. The time frame taken for the research is 8years. In taking non-economic factors for prediction of exchange rate, a primary survey is required for data collection. However, it is recommended that researcher can use other models like ANN, Correlation technique in determining the factors affecting exchange rate movements.

 

ACKNOWLEDGEMENT:

The author is grateful to the authorities of Department of Finance, Institute of Management, Christ University for the facilities.

 

CONFLICT OF INTEREST:

The authors declare no conflict of interest.

 

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Received on 04.05.2018          Modified on 21.06.2018

Accepted on 03.07.2018     ©AandV Publications All right reserved