The Lead-Lag Relationship between Industrial Production Index Volatility and Foreign Direct Investment
Authors:   Asia-Europe Perspective Association, Yoo Han-Soo 
Published: June 2018

Foreign direct investment(FDI) not only increases output and employment, but also transfers advanced technologies. That is, FDI may promote economic growth and enhance competitiveness. One of the most important determinants of FDI is the market size. Larger market size offers opportunities to realize economies of scale. Market size is a proxy for product demand the potential for growth. This study uses industrial production index(IPI) as a proxy to market size, and examines the relationship between industrial production index volatility and foreign direct investment. That is, this paper investigates whether the uncertainty of real economy activity of the host country influences the decision-making of foreign investors. Previous studies on this subject focus mainly on the relationship between IPI in level and FDI. On the contrary, this study is distinguished from prior studies in that it investigates the relationship between IPI volatility and FDI. When decisions have an irreversible factor, uncertainty about future outcomes plays a key role in the decision to invest. Because FDI generally involves irreversible costs in the foreign country, foreign companies wait for a more favorable economic environment before investing abroad. Therefore, irreversibility of investment expenditures renders the investment decisions of foreign companies sensitive to economic uncertainty.
The descriptive statistics show excess kurtosis, and the Jarque-Bera test statistic rejects the null hypothesis of normality for the unconditional distribution of the monthly IPI changes.
To test ARCH effects in the residuals, we employ ex ante ARCH LM test. The null hypothesis of ARCH LM test is that there is no ARCH up to q order in the residuals. This is a regression of the squared residuals on a constant and lagged squared residuals up to order q. Ex ante ARCH LM tests show that there are ARCH effects in the residuals. Therefore, GARCH type model is appropriate for analyzing this data.
The procedure used in this paper involves the following steps. First, to determine IPI volatility, I use the exponential generalized autoregressive conditional heteroskedasticity model proposed by Nelson. EGARCH model has a distinctive feature, namely, conditional variance is modeled to capture the asymmetry effect of volatility. The return series of IPI shows the serial correlation. This causes the distortion of parameter estimates in GARCH type model. To account for serial correlation in the returns, I include a MA(1) process for the residuals in the mean equation. According to the results, the asymmetric volatility effect is significant at 5% level. In this study, the asymmetry effect term is negative and statistically different from zero, indicating that the IPI volatility impact is asymmetric during the sample period. The economic implication of this sign of the leverage effect term is that a decrease in IPI would lead to a higher level of uncertainty when compared to the level of uncertainty generated by an increase in IPI. In estimated EGARCH model, the persistence parameter which is the coefficient of lagged conditional variance in the variance equation is smaller than one. Therefore, estimated conditional variance series is stationary. Ex post LM statistics test whether the residuals exhibit ARCH effect. If the EGARCH model is correctly specified, there should be no ARCH effect in the residuals. P-value indicates that there is not any ARCH effect up to order 5. Therefore, the EGARCH model is well specified.
Second, a prerequisite in applying the Granger causality test is to test the unit root properties of the series. To examine the stationarity of the series, the Augmented Dickey-Fuller test is applied. The optimum lag order is determined by the SC. ADF test results show that the null hypothesis of a unit root is rejected in cases of IPI volatility and FDI, respectively.