تاثیر نوسانات نرخ ارز بر جریان سرمایه‌گذاری مستقیم خارجی در ایران

جریان‌های سرمایه‌گذاری خارجی از عوامل اساسی در رشد اقتصادی کشورها در فرآیند جهانی‌شدن محسوب می‌شود. تحقیقات اخیر بر ‌روی نرخ ارز، اهمیت آن را به‌عنوان یکی از عوامل اصلی جریانات در تجارت و سرمایه‌گذاری مستقیم ‌خارجی (fdi) نشان می‌دهد. اگرچه نرخ ارز و fdi به‌طور تجربی مورد مطالعه قرار گرفته‌اند، اما نوع روابطی که بین نوسانات نرخ ارز و جریان سرمایه‌های بین‌المللی وجود دارد عمدتاً ناشناخته است. هدف اصلی این تحقیق بررسی تجربی عوامل موثر بر fdi ورودی، به‌ویژه نوسانات نرخ ارز برای اقتصاد ایران با استفاده از رویکرد همجمعی یوهانسن جوسیلیوس در دوره زمانی 4q2012-2q1980 (3q1391-1q1359) است. در این تحقیق، نوسانات نرخ ارز از طریق الگوی واریانس ناهمسانی شرطی اوتورگرسیون تعمیم یافته(garch) محاسبه شده است. نتایج حاصل از برآورد مدل نشان می‌دهد تاثیر متغیرهای تولید ناخالص داخلی، درجه بازبودن تجاری و نرخ ارز بر fdi‌ مثبت بوده و اثر متغیرهای فراریت نرخ ارز و قیمت‌ جهانی‌ نفت بر fdi منفی است. بر اساس نتایج تحقیق حاضر به سیاست‌گذاران اقتصادی توصیه می‌شود با به‌کارگیری سیاست‌های ارزی مناسب که منجر به پایداری هرچه بیشتر نرخ ارز و کاهش نوسانات نرخ ارز می‌شود شرایط را برای ثبات بیشتر اقتصاد فراهم کرده تا با تکیه بر آن بتوانند fdi بیشتری را جذب نموده و شرایط را برای رشد اقتصادی بیشتر فراهم آورند.

The Impact of Exchange Rate Volatility on Foreign Direct Invesment in Iran

Introduction Continuous growth and development in the economy need to attend its determinants. Investing or forming capital is the necessary condition for economic growth and development. The place and the role of investing in the mentioned processes are to the extent that investing is the motive engine in the economic growth. Currently, the situation of Iran apos s economy is in a way that savings and internal sources are not sufficient and attracting foreign capital seems to be the only useful and valid way (Komijani Abasi, 2006). The main goal of this study is to evaluate the determinants of inward FDI, particularly volatility of exchange rate in Iran, by using the JohansenJuselius integration system approach model covering the period of 1980Q22012Q4. In this research, the volatility of real exchange rate is obtained by Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) method. Effective Factors on Volatility of Exchange Rate and Its Relationship to FDIIn theoretical view, Dornbusch (1976) showed that forecasted monetary shocks through overshooting effect of exchange rate could create extreme volatility of exchange rate. In addition, Calderon (2004) maintained that the stability of monetary shocks is the only effective element on the variation of exchange rate and the nonmonetary elements including efficiency shocks and state expenses can affect them. Based on Frenkel and Mussa (1985), the continuous increase of state expenditures leads to a balanced increase of real exchange rate in the long run and consequently to the increase of net foreign equities. Similarly, state expenditures through influencing on the demand side of economy in short run can have a positive effect on real exchange rate. Cociu (2007) defines interest rate as one of the effective variables on the exchange rate volatility. Based on the macroeconomic subjects, variation in the interest rate leads to variation in inflation and exchange rate. Therefre, it is expected that by the increase of interest rate, the inward foreign investments increase and consequently the local money value increases. The other nonmonetary effective variables on the variation of real exchange rate are the efficiency growth. Cushman (1985) believed that the relation between the variation of exchange rate and FDI flow is in dependent to the place where the data have been purchased, products manufactured, fiscal capital emanated from and products sold. Econometric Model SpecificationBy considering most theories and tentative studies for identifying the determinants of inward FDI, emphasizing the economic factors of the country and also specific concerns of Iran’s economy, variables such as exchange rate, GDP, openness, world oil price, volatility of exchange rate can be introduced as the determinants of inward FDI in the econometric pattern below: (1)Estimating the GARCH Model of Iran apos s Exchange RateThe null hypothesis was rejected, showing that…(F=13.79 pvalue=0.0003).The results of the GARCH (1,1) estimated model can be seen as follows. According to the results, the effects of Garch are accepted.Table 1: Garch TestExchange rate autoregressive modelResiduals` variance of exchange rate autoregressive model Variables CofficientsVariablesCofficientsc20.59(0.00)c428.13(0.06) 1.003(0.00) 1.73(0.00)2.09 0.32(0.00)Source: The research results And so we have: Therefore, the volatility of real exchange rate are calculated using GARCH (1, 1) in the above equation.Results and DiscussionInvestigating the stationary of variables through Dicky Fuller unit root test all of them are static in the first order difference.Therefore, all of the variables are the convergence of degree one, I(1). Akaike Information Criterion (AIC) and Schwarz Criterion (SC) indicators can be used to determine the optimum lags. According to adjusted LR test, the order of 7 bases on (AIC) index is accepted. Usually for estimating the coefficients of the model and specifying the long run relationships, we need thetwo statistics of trace and max. Monte Carlo apos s studies reveal that when the residuals of equations have inordinate skewness or kurtosis, the trace test is more suitable than max test (Noferesti, 1999). We estimated the regulated standard from conditional form (Pattern 1) to the unconditional one (pattern5). Based on the results of this study, pattern 3 is the proper one for cointegration analysis. Moreover, based on this pattern, the existence of 3 cointegrated vectors is confirmed.Table (2) shows the coefficients of the cointegrated vectors that are explanatory of long run equilibrium relations between the model variables. Among these vectors, the coefficients of the third cointegrated vector are matched with the economic theories and have the expected signs. Table 2. Estimated Cointegrated Vectors in Johansen Estimation (in Brackets)VariablesVector 1Vector 2Vector 3Normalized vector 1Normalized vector 2Normalized vector 3LFDI0.950.110.631.001.001.00LYD0.400.450.130.424.210.21Os0.0210.0250.0010.020.240.002Op0.460.260.090.492.450.15Se0.000060.000060.00080.000060.0050.001E0.00010.00010.000060.00010.0010.0001Source: The research results Therefore, based on vector 3, we can express the long run relationship between the variables as below.LFDI = 0.21 LYD 0.002 OS +0.15 OP 0.001SE +0.0001 E (3)The estimated result shows that, based on the theoretical basis, FDI in Iran has a direct relation with GDP, openness and exchange rate variables and has a negative relationship with the volatility of exchange rate and world oil price. The IRF and FEVD tests confirm the estimated results of the long run relationship quite well. As a result, the occurrence of one shock in GDP, openness and exchange rate have a significant and positive effect on FDI.ConclusionEmpirical results show that openness, GDP, and exchange rate do have a significant and positive impact but volatility of exchange rate and world crude oil prices do have a significant and negative impact on the flow of inward FDI in Iran. Therefore, economical politicians should minimize the barriers of import through joining the world trade organization that is, through deregulation and reduction of tariffs, and at the same time, emphasizing production and nonoil exports especially industrial commodity, and promoting the foreign trade. For the world oil price, it is recommended that the goals of macroeconomic policies support the base and power of country production to increase the nonoil export and reduce the country apos s dependence on the world crude oil price. For the GDP, increasing the efficiency of internal resources and activating the nonused capacity to absorb more FDI.