Conditional Dynamic Dependence and Risk Spillover between Crude Oil Prices and Foreign Exchange Rates: New Evidence from a Dynamic Factor Copula Model

Round 1

Reviewer 1 Report

This paper analyze the relationship of crude oil and exchange rate markets. This is a widely researched topic and thus needs a strong motivation to distinguish it from existing literature. However, the current form on paper fails to address it.

The author should clearly distinguish the contribution from existing literature. The author needs to cite the related literature.

Furthermore, the author need to elaborate on the channels of spillover between the two markets.

The author mention that a lot of other methods are used to study this relationship but they did not mention how the choice of their method is better (connectedness approach, wavelet approach etc.).

Again, please cite existing literature and then highlight why the adopted method is better. The results and policy implication section needs a thorough revision as well. 

Author Response

Response to Reviewer #1:

 

We really appreciate for the reviewer’s efforts and well targeted comments. To address these comments, we have to great extent improved the quality of our paper. Please see detailed response listed below.

 

1. This paper analyzes the relationship of crude oil and exchange rate markets. This is a widely researched topic and thus needs a strong motivation to distinguish it from existing literature. However, the current form on paper fails to address it.

 

Re: Thanks for the reviewer’s valuable comments. We have tried our best to discuss the relationship of crude oil and exchange rate markets and distinguish it from existing literature in Introduction part of our revised manuscript as follows:

First, we have emphasized that there are many factors affecting the overall interdependence between crude oil market and foreign exchange market. All these factors affect the overall interdependence between crude oil market and foreign exchange market directly and indirectly. It is necessary for us to identify the common factors first and then capture the interdependence between crude oil price and exchange rates of oil trading countries, conditional on the common factors. It can help us to identify more precise dependence structure between crude oil market and foreign exchange market of both oil exporters and importers. Please see details in Paragraph 4 in Introduction part, Page 2.

Second, we have constructed the copula model conditional on the common factors of the crude oil market and exchange rate market of oil trading countries. Then this factor copula model can capture the dependence structure between the two markets more accurately. Please see details in Paragraphs 5-6 in Introduction part, Page 2-3.

Third, we have also identified the dynamic inter-market dependence patterns of the two markets as well as the respective volatility spillover effect in the framework of the factor copula model. Then we can provide new suggestions for the market regulations in oil trading countries. Please see details in Paragraphs 8-11 in Introduction part, Page 3-4.

 

 

2. The author should clearly distinguish the contribution from existing literature. The author needs to cite the related literature.

 

Re: Thanks a lot for the reviewer’s kind suggestions. To make the novelty of this paper clearer, we have revised the manuscript and illustrate our contributions to the existing literature as follows:

Indeed, the dependence between crude oil and exchange rate markets is a widely focused research topic. Most of the existing literature is a two-way study of oil price and exchange rate, focusing on their interdependence (Hussain et al., 2017; Tiwari et al., 2019; Guo and Ye, 2021). For example, Malik and Umar(2019), Huang et al. (2020) and Lin and Su (2020) studied from crude oil price to ex-change rate or exchange rate to oil price. Nevertheless, most of the existing research results directly investigate the interdependence between oil market and foreign exchange market. They didn’t consider the common factors related to these markets. We have found that there are many mixed factors affecting the dependence, the common factors which have been ignored from previous studies nevertheless. Therefore, this paper combines the factor analysis and copula model to reflect the dependence across crude oil and foreign exchange markets more accurately. Please see details in Paragraph 6 in Introduction part, Page 2.

To this end, we have made some contributions to further identifying the conditional dynamic dependence between oil prices and the exchange rates from a risk perspective. On the one hand, we try to measure the dependence structure between oil price and exchange rates of oil trading countries, conditional on the common factors related to the two specific financial market. On the other hand, we adopt the factor copula model to explore the dynamic conditional dependence structure between crude oil market and exchange rate market. Please see details in Paragraph 12 in Introduction part, Page 4.

 

Reference:

Hussain, M.; Zebende, G.F.; Bashir, U.; Donghong, D. Oil Price and Exchange Rate Co-Movements in Asian Countries: Detrended Cross-Correlation Approach. Phys. A Stat. Mech. its Appl. 2017, 465, 338–346, doi:10.1016/j.physa.2016.08.056.

Tiwari, A.K.; Trabelsi, N.; Alqahtani, F.; Bachmeier, L. Modelling Systemic Risk and Dependence Structure between the Prices of Crude Oil and Exchange Rates in BRICS Economies: Evidence Using Quantile Coherency and NGCoVaR Ap-proaches. Energy Econ. 2019, 81, 1011–1028, doi:10.1016/j.eneco.2019.06.008.

Guo, R.; Ye, W. A Model of Dynamic Tail Dependence between Crude Oil Prices and Exchange Rates. North Am. J. Econ. Financ. 2021, 58, 101543, doi:10.1016/j.najef.2021.101543.

Malik, F.; Umar, Z. Dynamic Connectedness of Oil Price Shocks and Exchange Rates. Energy Econ. 2019, 84, 104501, doi:10.1016/j.eneco.2019.104501.

Huang, S.; An, H.; Lucey, B. How Do Dynamic Responses of Exchange Rates to Oil Price Shocks Co-Move? From a Time-Varying Perspective. Energy Econ. 2020, 86, 104641, doi:10.1016/j.eneco.2019.104641.

Lin, B.; Su, T. Does Oil Price Have Similar Effects on the Exchange Rates of BRICS? Int. Rev. Financ. Anal. 2020, 69, 101461, doi:10.1016/j.irfa.2020.101461.

 

 

3. Furthermore, the author needs to elaborate on the channels of spillover between the two markets.

 

Re: Thanks for the reviewer’s valuable comments. We have added two paragraphs to elaborate on the channels of spillover between crude oil market and the exchange rate market.

We have further analyzed the relationship between crude oil price and exchange rates of oil trading countries, as well as the channels of spillover between the two markets. On the one hand, the crude oil price volatility can affect the real economy as well as financial markets through its effect on exchange rates. As to the crude oil futures price, it may have a positive correlation with the exchange rate of the respective importing countries but have a negative correlation with that of the exporting countries. Furthermore, as the main settlement currency in the international crude oil market, the US dollar will also inevitably affect the international crude oil price. On the other hand, when the US dollar appreciates, the crude oil price measured in their own currency will increases for the oil importing countries (Beckmann et al., 2020). Then the appreciation of the dollar increases the purchasing power and oil supply of oil-producing countries and increases their actual disposable income. In contrast, the depreciation of the dollar increases the purchasing power and oil demand of oil importing trading countries and, so the price of dollar-denominated crude rises (Qiang et al., 2019). To this end, the fluctuation of the U.S. dollar exchange rate generates risk spillover to the crude oil market, leading to volatility in its oil prices.  

Please see details in Paragraphs 2-3 in Introduction part, Page 2.

 

Reference:

Bénassy-Quéré, A.; Mignon, V.; Penot, A. China and the Relationship between the Oil Price and the Dollar. Energy Policy 2007, 35, 5795–5805, doi:10.1016/j.enpol.2007.05.035.

Beckmann, J.; Czudaj, R. Is There a Homogeneous Causality Pattern between Oil Prices and Currencies of Oil Importers and Exporters? Energy Econ. 2013, 40, 665–678, doi:10.1016/j.eneco.2013.08.007.

Beckmann, J.; Czudaj, R.L.; Arora, V. The Relationship between Oil Prices and Exchange Rates: Revisiting Theory and Evi-dence. Energy Econ. 2020, 88, 104772, doi:10.1016/j.eneco.2020.104772.

Qiang, W.; Lin, A.; Zhao, C.; Liu, Z.; Liu, M.; Wang, X. The Impact of International Crude Oil Price Fluctuation on the Ex-change Rate of Petroleum-Importing Countries: A Summary of Recent Studies. Nat. Hazards 2019, 95, 227–239, doi:10.1007/s11069-018-3501-y.

 

4. The author mention that a lot of other methods are used to study this relationship but they did not mention how the choice of their method is better (connectedness approach, wavelet approach etc.).

 

Re: Thanks for the reviewer’s kind comments. There are many methods to describe the dependence between crude oil and exchange rate, most of which use copula function. We focus on the reasons for using copula functions in the sixth paragraph of the Introduction part.

We have discussed how previous studies have focused on the relationship between oil prices and exchange rates in the 6^th paragraph of the Introduction part. Compared with other approaches, the copula model gives more accurate results in terms of dependence between the exchange rates of oil trading countries and the crude oil prices because it can capture the nonlinear dependence between non-normal return series. The copula function allows the dependence structure between individual return series to be studied separately in describing the joint distribution between two return series, so that it does not restrict the marginal distribution. This advantage provides a lot of freedom for the oil price and crude oil trading country exchange rate return series to be studied in this paper.

Furthermore, we try to identify the dynamic dependence structure and risk spillover effect between the crude oil price and foreign exchange rates of oil trading countries, conditional on the common factors related to the two markets. As we have discussed, there are some common factors related to both the oil market and exchange rate market, which will also affecting the overall interdependence between crude oil market and foreign exchange market directly and indirectly. However, the traditional approaches have ignored the influence from the common factors and then cannot give the precise measurement for the dependence structure among the financial markets. Then it is necessary for us to capture the interdependence among markets and measure the respective risk spillover effect, conditioned on common factors. Therefore, we adopt the factor copula model rather than other approaches such as connectedness approach, wavelet approach to capture the conditional dynamic dependence structure and risk spillover effect between the crude oil market and exchange rate markets of oil trading countries. Please see details in Paragraph 6 and Paragraph11 in Introduction part, Page 2.

 

5. Again, please cite existing literature and then highlight why the adopted method is better. The results and policy implication section needs a thorough revision as well.

 

Re: Thanks a lot for the reviewer’s kind suggestions. We have revised the manuscript. We have already cited existing literature and then highlight why the adopted method is better in the seventh to ninth paragraphs of the introduction. Please see details in Paragraphs 7-9 in Introduction part, Page 3-4.

  We have tried to make two main contributions to the relevant studies in this paper. On the one hand, we try to measure the dependence structure between oil price and exchange rates of oil trading countries, conditional on the common factors related to the two specific financial market. On the other hand, we adopt the factor copula model to explore the dynamic conditional dependence structure between crude oil market and exchange rate markets. Please see details in Paragraph 12 in Introduction part, Page 4.

And we have a thorough revision for the results and policy implication section. The added contents are as follows: It is beneficial for the policymakers and market regulators from oil trading countries to pay attention as well as to prevent risk spillovers from the crude oil market to their currency exchange markets. Especially, they can try their best to prevent the financial contagion in order to maintain the stability of global financial markets. At the same time, it can direct the financial institutions and market risk managers to adjust their oil exchange rate portfolio strategies to hedge against the extreme oil risks. Please see details in Paragraph 7 in the Conclusion part, Page 20.

Author Response File: Author Response.docx

Reviewer 2 Report

Thank you for the chance to review this paper. The paper broadly deals with the interaction between a number of exchange rates and 2 measures of oil prices.

I was ready to like this paper. The techniques were interesting and the topic potentially important. I really liked that you looked at distributions, rather than just running regressions and their variants (like ARIMAs). I would have loved a paper that started after page 13, talking about variances at the tails.

But as I read on, the numerous twists and turns -- and the heaping on of different measures and models in an effort to increase the complexity of the paper - made me dislike it to the point of requesting revisions.

The basic problem is that there are 2 papers here masquerading as one.

The paper presents 5 steps and several variants on those steps.

1. extract components from variables possibly affecting exchange rates (ERs),
2. look at the distribution of the factors, and the effect of lag values plus variance
3. select for each ER which has more explanatory power,,
4. put them in copulas and look variance, and
5. cut the copulas into a loss area, and look at the ways those pieces correlate with each other.

But we run every possible statistical procedure to figure out what is best. We four types of copulas. We have 12-ish exchange rates (which have assumed to tell us something about oil-exporters or importers simply by the country they are from). I say 12-ish because USD there is with the correct zero mean. We have a selection between 2 or 3 components. Then we we see the corrleations.

So the first paper ends at Figure 3 with the Kendall taus (and presumably you use the sinusoidal version to smooth it out? Its not told explicitly why we need equation (13).

I would remove all the unecessary complexity from that part. That includes the discussion of 2 vs. 3 components. The description of all the copulas in Table 1. Just choose one that performs well enough. The complexity cost of explaining all is much higher than any benefit from better data.

In my opinion, the whole discussion of VAR and CoVAR (from the literature and methods to the results) is a whole separate paper. A normal brain (at least for me) can handle the steps up to tau. But then to take the extra steps, and discuss these TYPES (plural) of covariance is too much.

Or if you really want the VAR/CoVar to be the discussion, then you must simplify the discussion of the previous 3 steps to get there.

Furthermore, the stilted and formal language makes reading the paper a drag. I just reviewed a similar paper, and the authors presented the same kind of material in far more engaging English - making the presentation far easier to follow.

I mention language also because there are about 2-3 errors per paragraph. And in many cases, the authors just get bored and stop typing (ie from p. 19, line 486 onward, "the two-factor copula model is superior to the three-factor copula and the Copula-486 GARCH model. Therefore, this paper captures the. Second, the common factors fluctuated greatly".

The writing just stops after "this paper captures the". Captures the what?

Another advantage of splitting this into 2 papers is that you can do justice to the division between oil exporters and importers. The discussion is rushed and I have no idea what to think about it. You assume that oil-exporting economies reflect oil exporters... which is okay if you have enough time and space to explain. But now, its just rushed over.

On the other hand, the methods and structure are right, concise and nice. The methods well presented. Its not a problem of each piece of this 5 part chain...just too many links.

So this paper reflects the same problems of all the papers I must review from China. These are:

a) turgid and difficultly formal English,

b) incomplete sentences and incorrect English,

c) too many models and methods thrown in, adding unnecessary complexity to the paper,

d) literature which is used to support the authors arguments, rather than to actually explain what these authors think, and

e) leaps to implications which are too far (ie, you try to go from covariations on copulas to separate implications for importers and exporters).

At a bare minimum (Energies accepts most papers), you would need to fix the English and reduce by 50% the complexity, hopefully by chopping the paper into 2) before I'd feel this is something worth reading. 

Like I said, I would have loved a paper only from p.13 onward. This tail analysis and ONLY including copulas for ERs and oil prices - without the extracted components and whole other discussion would have been good enough for me. Simplicity is beauty.

 

 

 

Author Response

Response to Reviewer #2:

 

Reviewer 2:

The basic problem is that there are 2 papers here masquerading as one.

The paper presents 5 steps and several variants on those steps.

• extract components from variables possibly affecting exchange rates (ERs),
• look at the distribution of the factors, and the effect of lag values plus variance
• select for each ER which has more explanatory power,,
• put them in copulas and look variance, and
• cut the copulas into a loss area, and look at the ways those pieces correlate with each other.

But we run every possible statistical procedure to figure out what is best. We four types of copulas. We have 12-ish exchange rates (which have assumed to tell us something about oil-exporters or importers simply by the country they are from). I say 12-ish because USD there is with the correct zero mean. We have a selection between 2 or 3 components. Then we see the correlations.

So the first paper ends at Figure 3 with the Kendall taus (and presumably you use the sinusoidal version to smooth it out? Its not told explicitly why we need equation (13).

 

Re: Thanks for the reviewer’s valuable comments. We have added the explanation of why we need equation (13). To illustrate the dependence structure more comparable, we convert the Pearson ρ to the Kendall τ. Please see details in the description of time-varying copula dependent parameter model used in the case study in Section 2.1.3, Page 7 and equation (14)-(17), Page 8.

 

I would remove all the unnecessary complexity from that part. That includes the discussion of 2 vs. 3 components. The description of all the copulas in Table 1. Just choose one that performs well enough. The complexity cost of explaining all is much higher than any benefit from better data.

 

Re: According to your comments, we have deleted the discussion of 3 components. Please see details in the description of extraction and analysis of common factors used in the case study in Section 4.1, Paragraph 1, Page 11.

 

In my opinion, the whole discussion of VAR and CoVAR (from the literature and methods to the results) is a whole separate paper. A normal brain (at least for me) can handle the steps up to tau. But then to take the extra steps, and discuss these TYPES (plural) of covariance is too much.

 

Re: Thanks a lot for the reviewer’s kind opinion. As indicated in the introduction part, crude oil price fluctuations and exchange rate fluctuations interact with each other and thus affect the real economy as well as the financial markets.

First of all, it can be seen from paragraphs 2-3 of the Introduction that oil prices and exchange rates affect each other and there is a risk transmission. Then, by reviewing previous studies, the VAR and CoVaR also measure the risk dependence, so the dynamic dependence and risk dependence between crude oil market and foreign exchange market are studied as a whole. Therefore, studying the dependence and risk spillover effects between the two markets can help major crude oil trading countries stabilize their financial markets and reduce risk spillover. We research the dynamic inter-market dependence patterns and volatility spillover effects in the framework of the factor copula model. Please see details in Paragraphs 5-6 and Paragraphs 10-11 in Introduction part, Page 2-4.

 

Or if you really want the VAR/CoVaR to be the discussion, then you must simplify the discussion of the previous 3 steps to get there.

 

Re: According to your comments, we have tried our best to adjust the content of the manuscript.

First, we take the Data and Summary Statistics as the third part of the paper.

Second, the second part of Methodology is divided into 2.1. Dynamic Factor Copula Model and 2.2. Spillover Measuring Model.

Third, the fourth part, as an empirical study, is divided into 4.1. Extraction and Analysis of Common Factors, 4.2. Model Estimations, 4.3. Analysis of dynamic dependence and 4.4. Analysis of risk spillover.

 

Furthermore, the stilted and formal language makes reading the paper a drag. I just reviewed a similar paper, and the authors presented the same kind of material in far more engaging English - making the presentation far easier to follow.

I mention language also because there are about 2-3 errors per paragraph. And in many cases, the authors just get bored and stop typing (ie from p. 19, line 486 onward, "the two-factor copula model is superior to the three-factor copula and the Copula-486 GARCH model. Therefore, this paper captures the. Second, the common factors fluctuated greatly".

The writing just stops after "this paper captures the". Captures the what?

 

Re: Thanks a lot for the reviewer’s kind suggestions. The efforts made here can help us better improve our language. We feel sorry for error. We have fixed the errors and the language has been touched up throughout the article. A proof reading by a native English speaker has been done before the resubmission, and we believe the revised version is with higher quality in English writing.

 

Another advantage of splitting this into 2 papers is that you can do justice to the division between oil exporters and importers. The discussion is rushed and I have no idea what to think about it. You assume that oil-exporting economies reflect oil exporters... which is okay if you have enough time and space to explain. But now, its just rushed over.

 

On the other hand, the methods and structure are right, concise and nice. The methods well presented. Its not a problem of each piece of this 5 part chain...just too many links.

So this paper reflects the same problems of all the papers I must review from China. These are:

1. a) turgid and difficultly formal English,
2. b) incomplete sentences and incorrect English,
3. c) too many models and methods thrown in, adding unnecessary complexity to the paper,
4. d) literature which is used to support the authors arguments, rather than to actually explain what these authors think, and
5. e) leaps to implications which are too far (ie, you try to go from covariations on copulas to separate implications for importers and exporters).

At a bare minimum (Energies accepts most papers), you would need to fix the English and reduce by 50% the complexity, hopefully by chopping the paper into 2) before I'd feel this is something worth reading.

Like I said, I would have loved a paper only from p.13 onward. This tail analysis and ONLY including copulas for ERs and oil prices - without the extracted components and whole other discussion would have been good enough for me. Simplicity is beauty.

 

Re: Thanks for the reviewer’s insightful comments. The efforts made here can help us better improve our work.

 As indicated in the introduction part, we should note that many common factors affecting both the oil market and exchange rate market need to be considered. However, relevant researches did not pay attention to common factors. And the distribution function of joint distribution in large dimensions is difficult to determine due to the stochastic nature of the futures market and the diversity of financial assets. There are many factors affecting the crude oil market and foreign exchange market, such as oil price volatility index (OVX), global consumer information index or a combination of the two factors, stock price index or stock market shock, and some exogenous variables that are not easy to be measured (political environment, interest rate, financial policy, etc.). All these factors affect the overall interdependence between crude oil market and foreign exchange market directly and indirectly. For example, both the global consumer information index and OVX are related to the whole economic expectation. Stock price index or stock market shock also reflect economic health, the financial health of individual companies, and current events. In particular, a similar explanation can be applied to consumer sentiment due to the COVID-19 pandemic, with WTI spillovers passing through to exchange rates from importing countries, whereas the oil prices are more closely correlated. It would, therefore, be useful to capture the interdependence among markets and measure risks conditioned on common factors. Please see details in Paragraph 4 and Paragraphs 10-11 in Introduction part, Page 2 and Page 4.

To this end, we have tried to make two main contributions to the relevant studies in this paper. On the one hand, we try to measure the dependence structure between oil price and exchange rates of oil trading countries, conditional on the common factors related to the two specific financial market. On the other hand, we adopt the factor copula model to explore the dynamic conditional dependence structure between crude oil market and exchange rate markets. Therefore, it is necessary to incorporate factor analysis into copula model. This paper combines the factor analysis and copula model to reflect the dependence across crude oil and foreign exchange markets more accurately. Please see details in Paragraph 12 in Introduction part, Page 4.

Author Response File: Author Response.docx

Reviewer 3 Report

I like paper. I think the study is well designed and the results are significant.
Some minor remarks
Line 251. The US is not a net oil importer, at least not for the entire period under review.
Line 308.Fig2 needs legend

Line 438. There is no reason to say that Russia  recovered quickly from the global crisis

line 449. Why does Chine represent major oil-trading country?

 

Author Response

Response to Reviewer #3:

 

Reviewer 3:

Line 251. The US is not a net oil importer, at least not for the entire period under review.

 

Re: We rewrite our statement in the data to avoid confusion. Please see details in Data and Summary Statistics, Page 9.

 

Line 308.Fig2 needs legend

 

Re: Thanks a lot for the reviewer’s kind suggestions. We have revised it and updated it in the paper. Please see the following figure for details. Please see details in Page 12, Line 392.

(a) oil-importing countries	(b) oil-exporting countries

Figure 2. Dynamic variation characteristics of common factors.

 

Line 438. There is no reason to say that Russia recovered quickly from the global crisis

 

Re: Thanks for the reviewer’s valuable comments. We have clarified our discussion about the dynamic dependence in Russia. In particular, the dynamic dependence for Russia is positive but it fluctuated below the zero line between 2012 and 2016. This finding suggests that rising oil prices contribute to the appreciation of the RUB during this period. However, the DZD-OIL is the opposite of the RUB-OIL. Instead, the dynamic dependence for Algeria is negative but it fluctuated above the zero line between 2010 and 2016. Please see details in Page 18, Line 517-522.

 

 

line 449. Why does China represent major oil-trading country?

 

Re: Thanks for your question. We have provided a detailed explanation of the reasons for this.

We have indicated that China is an important crude oil trading country, according to the report from International Energy Agency (IEA) (IEA, 2022). Please see details in the description of data and summary statistics used in the case study in section 2.1.3, line 335-336, Page 9.

Specifically, China is one of the largest producers and consumers of energy , and it imported 220 million tons of crude oil by May 2021, making it the world’s top crude oil importer. The world's top 10 oil producers address 72% of the world's demand, while the top five oil producers alone provide nearly 53% of global production. China produces the fifth largest amount of oil in the world, so you can imagine the importance of the position. Therefore , China represent major oil-trading country.

Furthermore, we have tried to give a detailed analysis of the dynamic dependence between the crude oil price and exchange rate of CNY in the empirical analysis. Please see details in the description of dependence in oil importers used in the case study in section 4.3.1, line 480-482, Page 16.

 

Reference:

IEA, Oil Market Report - February 2022. IEA. 2022, Paris https://www.iea.org/reports/oil-market-report-february-2022

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

The points raised are not adequately addressed. 

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 2 Report

Thank you for the chance to see this paper again.

It is better. Clearer. The English is still not great. It is understandable. But not pleasant to read.

It is still more complicated that I would have liked. For example, why use Brent and WTI?

Figure 4 -- the y-axes are not labelled. I have no idea what they are measuring.

You ended up adding more references, when less would have done.

I still dont understand why you present the 4 Copula models. They now dont appear in the paper. So why leave them in?

And you still havent explained why you categorize countries by oil-exporter. Some of these export and reimport.

In my opinion, the paper is stronger without the policy recommendation at the end. You say policymakers should find some way of managing the risk you find.

I should also mention, you addressed may half of my points. But the paper is clearly chopped up - and it has improved the new text. And I dont' want to go point-by-point again through my previous recommendations. I mention it because other reviewers in the future may not be so kind.

But as the second round is either accept or not - I suppose this article matches this journal's level. But I just cant accept it like it is, given that the English has not really been fixed.

I would request the authors address these minor changes. If doing so, no need to send back to me to check.

 

Author Response

Please see the attachment.

Author Response File: Author Response.docx