Increase in foreign direct investment

Increase in foreign direct investment (FDI) may decrease foreign portfolio investment volatility because it enhances the confidence of foreign investors and brings more investment in the home country (Gozgor & Erzurumlu, 2010). Similarly, Iyer, Rambaldi, and Tang (2003) found that FDI caused FPI while FPI did not cause FDI. Contrary to this, Ahmed and Malik (2012) concluded that direct investment found to granger cause by the portfolio investment in Pakistan only because its financial market is experiencing exponential progress (growth) and this factor will help in understanding the different investment environments. However, FPI was found to be non-consistent and non-persistent capital flow than FDI and other flows in crises times in some studies (Sarno & Taylor, 1999; Levchenko & Mauro, 2007). Thus, we expect significant relationship between FDI and foreign portfolio investment volatility.


Results and discussion
The results have been classified into tables. Table 1 reports descriptive statistics of our main variables and Table 2 presents GARCH results; it shows the effect of macroeconomic factors on FPI volatility. In Table 1, Pakistan has the highest inflation rate during the sample KPT-185 as compared to that of China, India and Srilanka. Pakistan has seen the highest interest rate on the average and it shows the severe problem of inflation On the other hand, China has the highest amount of foreign direct investment (131 billion dollars) and foreign portfolio investment (16.5 billion dollars, India has the second rank. China is the big economy and it has been able to attract more foreign investment. China has the highest economic growth, while India, Sri Lanka and Pakistan gets the second, third and fourth position in this regard. China has achieved tremendous industrial growth rate on the average, and Pakistan has obtained the second position.
In Table 2, the first equation is the mean equation and second is the variance equation of GARCH (1,1). The intercept of mean equation is negative and insignificant showing that there are no others factors influencing today׳s portfolio return. In mean equation, significant value of FPI(−1) implies that today׳s return is predicted by past return. The lag return value of FPI is significant at 1% level in case of all the countries; it means prior return of FPI predicts future return pattern of FPI. The residual term׳s coefficient is positively significant for all countries; it means that random term of previous day forecasts today׳s volatility. Therefore, we can say that there exists significant positive relationship between previous price behavior and current portfolio investment volatility.
The effect of foreign direct investment is negatively significant on volatility of FPI for three countries, namely, China, India and Pakistan. It implies that increase in FDI leads to reduction in FPI volatility. However, it has no effect in case of Srilanka because it has very lower level of FDI in the country. On the basis of these results, FDI has an important role to attract FPI in the country and it provides foundation for foreign portfolio investors to pursue FDI. Moreover, the significance of FDI for China, India and Pakistan shows that financial market is making progress and this would help understand different investment environments (Ahmed & Malik, 2012); insignificance of FDI in Srilanka implies that investors investing in Srilanka are facing liquidity problems (Gozgor & Erzurumlu, 2010).
In Table 2, the results of GDP growth rate are significant for China, Pakistan and Srilanka at 5%, 10% and 10% critical level. The results of China are more significant than those of Pakistan and Srilanka as China is growing rapidly; if we look at the average growth rate in Table 1, China has the highest average economic growth during the sample period. However, the results of Pakistan and Srilanka are moderately significant which indicate less attraction of GDP to foreign portfolio investors in these countries. Thus, Foreign portfolio flows are linked to higher GDP in China leading to reduction in volatility, and these results confirm to the results by Bekaert and Harvey (1998). Our result of GDPGR is against our expectation in case of Pakistan because GDP growth rate has no continuity and foreign investors are not attracted by the country׳s GDPGR.

equation View the MathML source Y o x

View the MathML source%Y=βo+β1×1+β2×2+β3×3+β12x1x2+β13x1x3+β23x2x3+β11×12+β22×22+β33×32
where Y KPT-185 estimate response, β0 is model constant, β1, β2 and β3 are linear coefficients, β12, β13 and β23 are interaction coefficients among the three factors, β11, β22 and β33 are quadratic coefficients and x1,x2 and x3 are independent variables
Table 1.
Levels and code of variables chosen for Box–Behnken design.
Variables Symbol Coded levels
Uncoded Coded ?1 0 +1
pH X1 x1 2 4 6
Dose (g) X2 x2 0.1 0.3 0.5
Contact time (min) X3 x3 30 90 180
Table options
A multiple regression analysis was done to obtain the coefficients KPT-185 and the equation could be used to estimate the response. A total of 15 experiments were needed to estimate the biosorption of Pb(II) on TPL. The accuracy of the proposed model is brain stem then identified by using analysis of variance (ANOVA). The property of fit polynomial model is represented by the coefficient of determination R2. The R2 values assure a measure of how variability in the observed response values can be clarified by experimental factors and their interactions ( Khajeh, 2011, Singh et al., 2010 and Yetilmezsoy et al., 2009).