*5.1. Volatility Measurement*

We first calculated the GARCH-based volatility for the exchange rate in terms of the monthly data and then converted it to annual data. The results and diagnostic tests for estimated the GARCH model are presented in Table A3 in the Appendix. Figure 3 depicts, on average, the yearly volatility of the bilateral exchange rate between Vietnam and 26 of its export partners in three different regions. The volatility fluctuates within a range of around 8%, the only exception being Italy. The magnitude of the fluctuation tends to be higher during the global crisis.

**Figure 3.** Exchange rate volatility over the 2000–2015 period.

### *5.2. Effects of Exchange Rate Volatility on Exports*

This section presents the estimation results pertaining to the effects of exchange rate volatility on manufacturing exports for the whole sample and in three regions—Asia, Europe, and America. We first show the result for the manufacturing sector, followed by its 10 subsectors.

Regarding the manufacturing sector, Tables 3 and 4 show the results of three kinds of panel unit root tests—IPS (Im et al. 2003), Maddala and Wu (1999), and Choi (2001)—for the whole sample and in three regions, respectively. At the manufacturing sector level, strong evidence of unit roots is found for the foreign GDP and real bilateral exchange rate, while, for the difference, the hypothesis that unit roots are present is strongly rejected. The variable for GARCH-based volatility is stationary for the whole sample and for Asia and America, but it contains unit roots in Europe. A similar pattern is found for manufacturing exports. Thus, the variables of interest are a mixed integration of I(0) and I(1).



Numbers indicate the *p*-values. A maximum of 2 lags were included.

### **Table 4.** The unit root test for three regions.


Numbers indicate the *p*-values. A maximum of 2 lags were included.

The cointegration tests introduced by Pedroni (1999, 2001) were performed to determine the long-run relationship between manufacturing exports and the other variables of interest. Table 5 displays the results for the whole sample and the three regions. Three statistics—group augmented Dickey-Fuller (ADF) test, panel ADF test, and group rho test—strongly support the hypothesis of cointegration. It is worth noting that the group small *t*-test and the group ADF test have a more powerful feature compared with other types of panel statistics, while the panel variance test and group rho test seem to perform poorly. Thus, based on this feature, we conclude that there is a long-run association among the given variables. An exception is the case for countries in the American region, as none of the calculated statistics are significant.


**Table 5.** The cointegration test for the full sample and three regions.

\*\*, \*\*\* indicate the hypothesis of no cointegration is rejected at significance levels of 5%, and 1%.

Next, we performed the long-run estimation among the relevant variables using the panel DOLS estimation in light of the confirmation by cointegration tests. Table 6 presents the estimation result for the whole sample and in three regions—Asia, Europe, and America. Overall, the foreign income is found to be positively related to Vietnam's exports for the whole sample and in Asia and Europe, as the estimated coefficients are significant at a level of at least 10%. The positive sign illustrates that an increase in income among Vietnam' trading partners enhances the exporting performance of manufactured goods for the country. This is consistent with the trade theory that a higher income in foreign nations will lead to an increase in domestic good demands. Also, Hooy et al. (2015) asserted that Vietnam has been deeply engaged in supply chain production due to the rise of China, enhancing both economic growth and exports. According to the results of the present study, the depreciation of the Vietnam Dong is not expected to cause an adverse impact on manufacturing exports in the long run. The effect is found to be negative, although insignificant, for the three regions. Exchange rate volatility has strong reverse effects on manufacturing exports, not only for the whole sample but also in America.


**Table 6.** The panel DOLS estimation for the full sample and three regions.

"Ln" represents variables defined in terms of logarithm. Standard errors are numbers in the parentheses. \*, \*\*, and \*\*\* indicate the 10%, 5% and 1% significance level, respectively.

We also examined the short-run relationship on the basis of the ECM model using Equation (3). The results are shown in Table 7, which paint a completely different picture of the effect of exchange rate volatility on manufacturing exports in Vietnam. The coefficients of the lagged error correction terms are negative and significant, supporting the long-run cointegration tests above. The foreign GDP and the bilateral real exchange rate have a positive association in the estimation for the whole sample and Asia. Europe has a positive significant coefficient for foreign income. The volatility of the exchange rate, on average, has no impact on exports in general in Asia, Europe, and America. The dummy variable representing the participation in the WTO is significant only for the case of Asia, implying that Vietnam gained a significant benefit in exporting goods to Asian countries. The global financial crisis is expected to be harmful, to some extent, to exports. The evidence of negative effects of volatility is weak, suggesting that it can be mostly insured against at low cost. Meanwhile, the price mechanism works via the real exchange rate to ensure that export supply equals demand. These findings imply that the manufacturing exports in Vietnam rely heavily on the partner's income and largely benefit from the depreciation of the Vietnam Dong.


**Table 7.** The panel OLS estimation for the full sample and three regions.

Δ represents variables defined in terms of difference, indicating growth rate. "Ln" represents variables defined in terms of logarithm. Standard errors are numbers in the parentheses. \*, \*\*, and \*\*\* indicate the 10%, 5% and 1% significance level, respectively.

When it comes to manufacturing exports at disaggregated levels, we applied the same econometrics procedures to all 10 subsectors for the whole sample and for the three regions. Before the panel DOLS and the panel OLS estimations were run, unit root tests for stationarity and panel cointegration tests were conducted.<sup>2</sup>

The long-run effects of exchange rate volatility on each subsector for the whole sample and in the three regions are depicted in Tables 8 and 9, respectively. Based on all of the data in Table 8, 8 out of 10 manufacturing subsectors suffered adverse effects due to exchange rate volatility. The coefficients of

<sup>2</sup> For minimizing space, the estimated results will be provided upon request. The findings for the 10 subsectors are similar to those for the manufacturing sector. The long-run relationship is confirmed for all 10 subsectors.

two subsectors, namely, *textiles, wearing apparel, leather, and related* products and *chemicals, rubber, plastics, and fuel products*, are statistically insignificant although negative. The effect of the bilateral real exchange rate is also found to be negative in five subsectors in the long run. Thus, a depreciation policy in Vietnam would lead to a decline in export value in the long term as it generates volatility in the exchange rate.

When the geographical factor is taken into consideration, we observe a completely different picture of the relationship between exchange rate devaluation, exchange rate volatility, and export performance at the subsector level. As can be seen from Table 9, exchange rate volatility has almost no effect on exports in Asia and Europe, but America has five subsectors that are negatively related to the exchange rate volatility.<sup>3</sup> It is only the subsector of *non-metallic mineral* products in the American region that enjoys a favorable gain from exchange rate depreciation without being influenced by the exchange rate fluctuation.

Using Equation (3), the short-run effects of exchange rate volatility on export performance at disaggregated levels were regressed and are provided in the next four tables with the application of panel estimation. Table 10 presents the result for the whole sample. The bilateral real exchange rate has a favorable impact on exports in such subsectors as (i) *textiles, wearing apparel, leather, and related products* and (ii) *furniture and other manufacturing products*. This implies that depreciation boosts exports in the short run rather than in the long run in some subsectors. Similarly, exchange rate volatility is found to be positively associated with exports for the subsector of *transport equipment.* Exporters in this subsector may pursue a strategy of exporting more in order to maintain its trading value, as hypothesized by De Grauwe (1988). The subsector of *chemicals, rubber, plastics, and fuel products* is found to be quite sensitive to exchange rate volatility in the short run, given that its estimated coefficient is positive in the current period but becomes negative in the first lag. We find no statistically significant effect of exchange rate depreciation and volatility on export values for the eight remaining subsectors.

A significantly different pattern is seen in Asia, Europe, and America, as indicated in Tables 11–13, respectively. The short-run effects of exchange rate volatility vary considerably across the subsectors, as well as in the given regions. Asia has three subsectors that are positively associated with exchange rate volatility, together with one that reacts negatively. In Europe, the number of subsectors experiencing favorable and harmful effects is three and two, respectively. In America, 3 out of 10 subsectors are found to be positively related to exchange rate fluctuations. Interesting to note is that the export performance in the subsector of *Transport equipment* is observed to benefit from the exchange rate fluctuations, as its estimated coefficients are statistically positive in all three regions. There is an increase in the export performance of *textiles, wearing apparel, leather, and related products* to countries in Europe and America when the exchange rate is volatile. The *Paper and printing* subsector is negatively related to the exchange rate fluctuations in Asia but positively associated in Europe. Other subsectors, such as (i) *wood and products of wood and cork,* (ii) *Chemicals, rubber, plastics, and fuel products*, (iii) *Non-metallic mineral products*, and (iv) *Furniture and other manufacturing products* are influenced by exchange rate volatility, either positively or negatively.

In the short run, the impact of the bilateral exchange rate on the export performance at disaggregated levels is considerably different across the regions. When the real bilateral exchange devaluates, it raises Vietnam's export value in three subsectors—*textiles, wearing apparel, leather, and related products*; *wood and products of wood and cork*; and *paper and printing*—to countries in Europe and America, and it has no effect on the remaining subsectors. In Europe, the estimated results indicate that two subsectors are positively influenced by exchange rate fluctuations, another two react negatively, and results for the rest of the subsectors are inconclusive. All results prove that the impact of the exchange rate on exports at the manufacturing disaggregated level depends on two factors: (i) the type of export and (ii) the exporting destination.

<sup>3</sup> Table A4 presented in indicates the percent change in exports for the manufacturing subsectors resulting from a 1% increase in exchange rate volatility.



"Ln" represents variables defined in terms of logarithm. ex1—Food products, beverages and tobacco; ex2—Textiles, wearing apparel, leather and related products; ex3—Wood and products of wood and cork; ex4—Paper and printing; ex5—Chemicals, rubber, plastics and fuel products; ex6—Non-metallic mineral products; ex7—Basic metals and fabricated metal products; ex8—Machinery and equipment; ex9—Transport equipment; ex10—Furniture and other manufacturing. Standard errors are numbers in the parentheses. \*, \*\*, and \*\*\* indicate the 10%, 5% and 1% significance level, respectively.



products of wood and cork; ex4—Paper and printing; ex5—Chemicals, rubber, plastics and fuel products; ex6—Non-metallic mineral products; ex7—Basic metals and fabricated metal products; ex8—Machinery and equipment; ex9—Transport equipment; ex10—Furniture and other manufacturing. Standard errors are numbers in the parentheses. \*, \*\*, and \*\*\* indicate the 10%, 5% and 1% significance level, respectively.



fabricated metal products; ex8—Machinery and equipment; ex9—Transport equipment; ex10—Furniture and other manufacturing. Standard errors are numbers in the parentheses.

\*, \*\*, and \*\*\* indicate the 10%, 5% and 1% significance level, respectively.

*J. Risk Financial Manag.* **2019**, *12*, 12



fabricated metal products; ex8—Machinery and equipment; ex9—Transport equipment; ex10—Furniture and other manufacturing. Standard errors are numbers in the parentheses.

\*, \*\*, and \*\*\* indicate the 10%, 5% and 1% significance level, respectively.



fabricated metal products; ex8—Machinery and equipment; ex9—Transport equipment; ex10—Furniture and other manufacturing. Standard errors are numbers in the parentheses. \*, \*\*,

and \*\*\* indicate the 10%, 5% and 1% significance level, respectively.



fabricated metal products; ex8—Machinery and equipment; ex9—Transport equipment; ex10—Furniture and other manufacturing. Standard errors are numbers in the parentheses. \*, \*\*,

and \*\*\* indicate the 10%, 5% and 1% significance level, respectively.
