Round 1

Reviewer 1 Report

This paper examines how volatility in the exchange rate between the dollar and yuan affects US-EU trade. It incorporates a possibility of asymmetric effects into the analysis.

 

Comments

 

1.     I am not sure how much I learned from this paper. It is a complicated analysis involving a comparison of linear and nonlinear models, distributed lags of various length, disaggregated industries, and allowing asymmetric effects of volatility rising versus falling. Having read the paper, I am still not sure I could summarize what the key result is that the authors want the reader to take away from the paper. Perhaps after the analysis, the authors could choose the model that fits the data best overall and perform a simulation.  Raise US-China exchange rate volatility by 10% and use the estimated coefficients in the model to project how US-EU trade is affected in each industry. Then aggregate those predictions over all the industries and generate a predicted change in overall US-EU trade. Some simulations like that would help the reader understand how overall US-EU trade is affected by US-China exchange rate volatility.

2.     The paper needs to make it clearer why the analysis is done separately for 12 industries rather than aggregating trade flows. Is there a theoretical reason why we would expect the effect of exchange rate volatility to differ by industry?  Running the regressions separately for different industries makes it harder to see the overall pattern in the results.  Often, the authors indicate that x out of 12 industries have at least one significant coefficient among the one to three lags, but the coefficients are sometimes positive and sometimes negative. Thus, it is rarely clear when the tables are being discussed whether third country exchange rate volatility tends to raise or lower trade between the US and EU.  The conclusion is clearer that an increase in yuan-dollar volatility raises US exports to the EU, but it is hard to see that in the tables.

3.     The paper is examining trade the US and EU, and it presents figure 1 showing US-EU real exchange rate volatility.  Presumably that measures the volatility in the exchange rate between the dollar and the euro.  But not all of the EU countries use the euro.  Are the US-EU trade figures really measuring US – euro area trade, or does the trade include all the EU countries? If the latter, how is the exchange rate volatility measured? Does it include all of the EU currencies or just the euro? I suspect that the trade figures include all of the EU but the volatility measures are based on the dollar-euro exchange rate only. The paper mentions dollar-euro volatility but never mentions the euro area when discussing trade.  Thus, it seems to be using an exchange rate volatility measure that is not accurately reflecting the currencies of all the EU countries.

4.     Figure 2 shows that there is a large spike in dollar-yuan volatility right around the time of the 2008 financial crisis.  Since many other factors changed around then that influenced trade flows, I worry that the coefficient on volatility is picking up the broader effects of the financial crisis. To their credit, the authors do attempt to control for country income in the analysis.  But the regressions would not control for any changes in trade barriers that might have happened during the financial crisis and that would have influenced trade flows.

Author Response

Response Letter to the Editor and the Referee Report on “Examining the Asymmetric Effects of Third Country Exchange Rate Volatility on Trade between the U.S. and the EU” (Manuscript Number: jrfm-1761508)

We totally agree with the suggestions of the editor, and the constructive comments and suggestions raised by the referee that help us rewriting a revised version of the paper. We trust that we have addressed in a satisfactory manner all the issues raised. The details of each of the changes made in the revised version of our paper are provided below. The revised portions were marked in red, and the point-by-point responses were listed as below. We believe that the revised manuscript meets the publication standards of JRFM.

 

Referees 1’s Comments

1. I am not sure how much I learned from this paper. It is a complicated analysis involving a comparison of linear and nonlinear models, distributed lags of various length, disaggregated industries, and allowing asymmetric effects of volatility rising versus falling. Having read the paper, I am still not sure I could summarize what the key result is that the authors want the reader to take away from the paper. Perhaps after the analysis, the authors could choose the model that fits the data best overall and perform a simulation.  Raise US-China exchange rate volatility by 10% and use the estimated coefficients in the model to project how US-EU trade is affected in each industry. Then aggregate those predictions over all the industries and generate a predicted change in overall US-EU trade. Some simulations like that would help the reader understand how overall US-EU trade is affected by US-China exchange rate volatility.

Authors’ Reply:

Thank you very much for your comments. In order to make the conclusions of this paper clear, we rewrote and rearranged the conclusions on page 21. We emphasize three key points. 1. Third country exchange rate fluctuations have an impact on bilateral trade. 2. The asymmetric effects exist, which can prevent the increased and decreased exchange rate volatility from offsetting each other in the symmetric analysis. 3. Different industries carry distinctive behaviors regarding exchange rate risk (e.g., Bredin et al., 2003; Wong and Lee, 2016; Nyambariga, 2017; Bahmani-Oskooee and Gelan, 2018). Furthermore, increased third country exchange rate volatility will increase bilateral trade. This paper aims to examine the impact of exchange rate fluctuations on trade. We evaluate 12 sectors only. It is unsuitable for simulating the overall trade situation, but we will include it in future research. Please see the final paragraph of the conclusion.

 

2. The paper needs to make it clearer why the analysis is done separately for 12 industries rather than aggregating trade flows. Is there a theoretical reason why we would expect the effect of exchange rate volatility to differ by industry?  Running the regressions separately for different industries makes it harder to see the overall pattern in the results.  Often, the authors indicate that x out of 12 industries have at least one significant coefficient among the one to three lags, but the coefficients are sometimes positive and sometimes negative. Thus, it is rarely clear when the tables are being discussed whether third country exchange rate volatility tends to raise or lower trade between the US and EU.  The conclusion is clearer that an increase in yuan-dollar volatility raises US exports to the EU, but it is hard to see that in the tables.

Authors’ Reply:

Thank you very much for your comments. We have added why 12 industries are done the analysis on page 6.  In table 8, all the coefficient of negative changes is greater than positive changes. It implies that the increase in the real exchange rate (dollar-yuan) volatility will boost U.S. exports to and imports from the EU in the long run. Please see lines 334 to 348.

 

3. The paper is examining trade the US and EU, and it presents figure 1 showing US-EU real exchange rate volatility.  Presumably that measures the volatility in the exchange rate between the dollar and the euro.  But not all of the EU countries use the euro.  Are the US-EU trade figures really measuring US – euro area trade, or does the trade include all the EU countries? If the latter, how is the exchange rate volatility measured? Does it include all of the EU currencies or just the euro? I suspect that the trade figures include all of the EU but the volatility measures are based on the dollar-euro exchange rate only. The paper mentions dollar-euro volatility but never mentions the euro area when discussing trade.  Thus, it seems to be using an exchange rate volatility measure that is not accurately reflecting the currencies of all the EU countries.

Authors’ Reply:

Thank you for your comments. Not all EU members use the euro is a limitation of our research, but 19 of the 27 EU members use the euro, which is as high as 70%. Furthermore, the previous literature of Ekanayake et al., 2011 did a similar study on the impact of the euro exchange rate on EU imports and exports. We added a footnote of which the EU members are on page 3. 3. At the same time, we will put it into the research limitations, please see the final paragraph of the conclusion.

 

4. Figure 2 shows that there is a large spike in dollar-yuan volatility right around the time of the 2008 financial crisis.  Since many other factors changed around then that influenced trade flows, I worry that the coefficient on volatility is picking up the broader effects of the financial crisis. To their credit, the authors do attempt to control for country income in the analysis.  But the regressions would not control for any changes in trade barriers that might have happened during the financial crisis and that would have influenced trade flows.

Authors’ Reply:

Thank you very much for your concerns about the 2008 financial crisis. We added a dummy variable (D08) during 2008 in the models of our paper. We found that the estimated results with dummy and without dummy did not show a significant difference in exchange rate volatility. Please refer to Appendix 1 and 2 for the long‐run coefficient estimates on the U.S. exports to and imports from the EU with dummy and third country effects in the nonlinear ARDL models.

 

5. Moderate English changes required.

Authors’ Reply:

Thanks for the referee’s concern. This revised version has been edited by MDPI Language Editing.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments

 

Journal of Risk and Financial Management (jrfm)-1761508

 

Examining the Asymmetric Effects of Third Country Exchange Rate Volatility on Trade between the U.S. and the EU

This paper aims to examine the symmetric and asymmetric effects of the third country exchange rate volatility on the trade flows between the U.S. and EU from January 2003 through March 2021. They find that separating increased volatility from declines and introducing nonlinear adjustment of the volatility shows a more significant outcome than symmetric analysis. Different industries carry distinctive behaviors regarding exchange rate risk, and the third country effect plays a vital role in trade between the U.S. and EU. It shows that increased Chinese yuan-U.S. dollar real exchange rate volatility induces U.S. exporters to boost export to the EU. Decreased third country exchange rate fluctuations will cause the U.S. to reduce imports from the EU.

 

1.       The period of sample is from 2003 to 2021. This period includes several important events (financial crisis of 2008, pandemic crisis, 2020) that affected all economies worldwide. However, there are no dummies variables in the models of the paper.

2.       In Table 2. Results of unit root tests. There is no explanation for the unit root test results.

3.       The authors could have in a separate subsection the diagnostic tests they use to examine the suitability of their models and analyze the test results.

4.       The authors should put more effort and thoroughly discuss point estimates, estimated effects, and the intuition behind the results backed up by the literature.

5.       What is the methodological innovation in their studies? How do they overcome the methodological issue in the previous research?

6.       The authors do not justify the selection of the present model and variables.

 

 

7.       The authors should further analyze the tests for the existence of asymmetries in the variables. In addition, to make clear the process, they follow to check for the existence of an asymmetric long-run relationship between variables (cointegration). What tests are used, and what do the results show?

Author Response

Response Letter to the Editor and the Referee Report on “Examining the Asymmetric Effects of Third Country Exchange Rate Volatility on Trade between the U.S. and the EU” (Manuscript Number: jrfm-1761508)

We totally agree with the suggestions of the editor, and the constructive comments and suggestions raised by the referee that help us rewriting a revised version of the paper. We trust that we have addressed in a satisfactory manner all the issues raised. The details of each of the changes made in the revised version of our paper are provided below. The revised portions were marked in red, and the point-by-point responses were listed as below. We believe that the revised manuscript meets the publication standards of JRFM.

 

Referees 2’s Comments

This paper aims to examine the symmetric and asymmetric effects of the third country exchange rate volatility on the trade flows between the U.S. and EU from January 2003 through March 2021. They find that separating increased volatility from declines and introducing nonlinear adjustment of the volatility shows a more significant outcome than symmetric analysis. Different industries carry distinctive behaviors regarding exchange rate risk, and the third country effect plays a vital role in trade between the U.S. and EU. It shows that increased Chinese yuan-U.S. dollar real exchange rate volatility induces U.S. exporters to boost export to the EU. Decreased third country exchange rate fluctuations will cause the U.S. to reduce imports from the EU.

1. The period of sample is from 2003 to 2021. This period includes several important events (financial crisis of 2008, pandemic crisis, 2020) that affected all economies worldwide. However, there are no dummies variables in the models of the paper.

Authors’ Reply:

Thank you very much for your concerns about the 2008 financial crisis. We added a dummy variable during 2008 in the models of our paper. We found that the estimated results with dummy and without dummy did not show a significant difference in exchange rate volatility. Please refer to Appendix 1 and 2 for the long‐run coefficient estimates on the U.S. exports to and imports from the EU with dummy and third country effects in the nonlinear ARDL models.

 

2. In Table 2. Results of unit root tests. There is no explanation for the unit root test results.

Authors’ Reply:

Thank you very much for your comments. We well noted and rewrote results of the unit root tests at lines 201 to 204 on page 7.

 

3. The authors could have in a separate subsection the diagnostic tests they use to examine the suitability of their models and analyze the test results.

Authors’ Reply:

Thank you very much for your advice. Having the diagnostic tests in a separate subsection help the paper's understanding. We rearranged the diagnostic tests in subsections 3.2.5 and 3.2.7 on pages 16 and 20.

 

4. The authors should put more effort and thoroughly discuss point estimates, estimated effects, and the intuition behind the results backed up by the literature.

Authors’ Reply:

Thank you very much for your advice. We emphasized our findings, rewrote our estimates, and cited literature to support our findings on pages 15 and 19.

 

5. What is the methodological innovation in their studies? How do they overcome the methodological issue in the previous research?

Authors’ Reply:

This paper presents the quantitative impact of exchange rate fluctuations on exports and imports, which is relatively new and less used. For example, the largest industry code 84, with an import share of 16.28%. The estimated coefficient is -0.0777, which implies that a 1% increase in the CNY/USD volatility hurts the exports of bilateral trade by 0.08%.

If aggregated data is used, it will not be able to see that different industries may respond differently to exchange rate fluctuations. So disaggregated data is used. In addition, most of the traditional studies use the linear ARDL model. This paper further considers that the increased and decreased exchange rate fluctuations have different impacts on different industries.

 

6. The authors do not justify the selection of the present model and variables.

Authors’ Reply:

We used models by Bahmani-Oskooee et al. (2013) augmented with the volatility measure of a third country. Furthermore, the diagnostic tests showed that most statistics are insignificant, meaning most models are correctly specified. Besides, almost all F-statistic results showed co-integration between all variables.

 

7. The authors should further analyze the tests for the existence of asymmetries in the variables. In addition, to make clear the process, they follow to check for the existence of an asymmetric long-run relationship between variables (cointegration). What tests are used, and what do the results show?

Authors’ Reply:

If n5 is not equal to n6 in eq. (8), implying that the POS could accept a different lag order than the NEG. Then an asymmetry hypothesis is supported. If the coefficient of  is different with , the short-run asymmetric effects of increased volatility versus decreased volatility will be supported. Our empirical analysis in table 7 and 8 shows that most n5 is not equal to n6 in eq. (8) and most coefficient of  is different with , the asymmetry assumptions are supported. Furthermore, the strong evidence of asymmetric by the Wald test rejects the null hypothesis =  for the short-run,  for the long-run model in eq. (8) and =  for the short-run,  for the long-run model in eq. (9). Please see pages 16 and 21.

 

8. Moderate English changes required.

Authors’ Reply:

Thanks for the referee’s concern. This revised version has been edited by MDPI Language Editing.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

The present version of paper is accepted.