Sustainability, Uncertainty, and Risk: Time-Frequency Relationships

Round 1

Reviewer 1 Report

Dear authors, the subject of your paper is interested. Attached our review report to improve your paper to follow point-by-point :

1. Title: to be arranged we can add (Empirical evidence with bibliometric analysis)

2. Abstract: this apptoach can be followed ( Aim- Methodology- Technique Method- Findings-implication policy - original value- recomendation)

3.  Theory/ Lietrature review:

* ''scientometric techniques'':  Provide some cited references more recent to this concept (Authors who have used this approch/method in bibilimetrix index)

* Review Databases [Scopus & WoS]: More researchers and  authors used Scopus database in their bibliometric analysis. But in this study WoS databases has been used, It did not made data issues in emerging the data sources from datat to the other? Explain more.

* The search equation was: (TI- 99 TLE (sustainability) AND TITLE (uncertainty OR risk*) AND TITLE-ABS-KEY (co-move- 100 ments OR link* OR dependence OR connectedness OR interdependence OR relationship* 101 OR correlation* OR connection*)) : is it the same equation model used for Scopus database and WoS? Or there were some specific keywords?

* Mendeley reference manager used to merge the databases from the two Open sources Scopus & WoS? Why this reference software used compared to other reference software?

* Duplicate records were removed during the screening process, resulting in a final analysis of 265 studies : What is the technique used to remove the DUPLICATE records?

* For Risk papers published we recommend these studies to improve the theory section:  

Ibtissem Missaoui, Mohsen Brahmi and Jaleleddine BenRajeb (2018). Quantitative relationship between corruption and development of the Tunisian stock market. Public and Municipal Finance, Vol.7, No. (2), 39-47. doi:10.21511/pmf.07(2).2018.04

Ibtissem Missaoui; Mohsen Brahmi; Jaleleddine Ben Rajeb. Ownership structure management and its effect on dividend policy in the Tunisian stock exchange enterprises: an empirical study, International Journal of Technology Transfer and Commercialisation, 2022 Vol.19 No.1, pp.83 - 96. DOI: 10.1504/IJTTC.2022.123084

4. Methodology :

 * Variables (sustainability, uncertainty, and risk): Control varibles provide more statistical significance when they added.

 * ( line 233) : ''all daily returns demonstrate a negative bias and display  high values for the Kurtosis statistics, indicating heavy-tailed distortions in the data' : explain this negative bias result found?

* Dickey-Fuller (ADF) demonstrate that all return series are stationary : provide from your table1 the illustrated result (give the P-value and compare it to the theory of ADF).

* the same thing for the tests : JB  & LBQ.

 * Wavelet Analysis : ( known as multi-resolution decomposition): Provide Cited reference authors who used this statistic approach.

* Wavelet Coherence : ??? 2 (?, ?) serves as a scale-specific squared correlation between the two series :  The result of tis test can be arranged in statistical table?

5. Application and Results:

* Unconditional Correlation Analysis: MOVE Index (RMOVE) is negative (-33%), and the Cboe Volatility Index (RVIX) is negative too (-63%) : What are this implication findings to the sustainability and other variables?

* Wavelet Power Spectrum : "The Cboe Volatility Index (RVIX) behavior, as depicted in Figure 5b, demonstrates notable volatility during 2020 and the beginning of 2023" : Compare this period (Ukrain/Russia ware)  to Covid pandemic (2019-2021) in term of volatility and inflation VIF index.

* Wavelet Coherence Approach : 

''....all the periods analyzed, indicating that in the short- medium- and long- term, the DJ Sustainability World Index (RW1SGI) significantly affected the Cboe Volatility Index (RVIX) negatively'' : Explain this result and the real impact to the financial Market and uncertanity indexes?

* "the direction of causality changed during different periods and frequencies. 432 From 2014 to 2023, for the intervals of 2014, 2019-2020, 2021, and 2022-2023" : What is the rised period from thoses cited and your macroeconomic interpretation?

6.  Limitation section?

7. Conclusion and recomendations

8. References:  all new recent cited references in the text (theory section)  must be added in this section.

 

-

Good revision

 

Moderating english language revision ( speeling)

Author Response

August 25, 2023

Reviewer 1

Sustainability Journal

 

Re: Manuscript title: Sustainability, uncertainty, and risk: Time-frequency relation-ships

 

Ref.: Manuscript ID: sustainability -2563039

 

Dear Professor:

 

Thank you for your independent review report submitted dated August 7, 2023. We are very grateful for the comments and constructive suggestions raised by you. We have reviewed them all carefully and have revised the manuscript accordingly. Please see our detailed response below:

1. Title: to be arranged we can add (Empirical evidence with bibliometric analysis).

Thank you very much for your valuable comment. Although we recognize the strength of the literature review that was conducted, all authors discussed this aspect and decided not to include bibliometric analysis in the title because the final objective of the work was not such an analysis. However, we added bibliometric analysis in the keywords. Please see highlighted in green.

2. Abstract: this approach can be followed (Aim- Methodology- Technique Method- Findings-implication policy - original value- recommendation).

Thank you for the observation. This suggestion was validated, and the respective adjustments were made. Thank you very much for the excellent recommendation. Please see highlighted in green.

3. Theory/ Literature review:

* ''scientometric techniques'':  Provide some cited references more recent to this concept (Authors who have used this approach/method in Bibliometrix index).

Thank you for the observation. Novelty, these methods of analysis had not been used before for the analysis of this particular subject. Please see highlighted in green.

* Review Databases [Scopus & WoS]: More researchers and  authors used Scopus database in their bibliometric analysis. But in this study WoS databases has been used, It did not made data issues in emerging the data sources from data to the other? Explain more.

Thank you for the observation. In addition to these two databases, Google scholar was also searched. Thus, we include some articles from the gray literature. For example, (Rua, 2012). Please see highlighted in green.

Rua, A. (2012). Wavelets in economics. Economic Bulletin and Financial Stability Report Articles, 8, 71–79.

* The search equation was: (TITLE (sustainability) AND TITLE (uncertainty OR risk*) AND TITLE-ABS-KEY (co-movements OR link* OR dependence OR connectedness OR interdependence OR relationship* OR correlation* OR connection*)) : is it the same equation model used for Scopus database and WoS? Or there were some specific keywords?

Thank you for the observation. The search equation used in WoS is included in the text: sustainability (Title) and uncertainty or risk* (Title) and co-movements OR link* OR dependence OR connectedness OR interdependence OR relationship* OR correlation OR connection (Topic). Please see highlighted in green.

* Mendeley reference manager used to merge the databases from the two Open sources Scopus & WoS? Why this reference software used compared to other reference software?

Thank you for the observation. Mendeley is part of Scopus and is one of the most widely used reference managers for processing bibliographic information.

* Duplicate records were removed during the screening process, resulting in a final analysis of 265 studies : What is the technique used to remove the DUPLICATE records?

In R, when merging two bibliographic databases, the following instruction is used:

wos_scopus<-mergeDbSources(wos_data, scopus_data, remove.duplicated = T)

* For Risk papers published we recommend these studies to improve the theory section:  

Ibtissem Missaoui, Mohsen Brahmi and Jaleleddine BenRajeb (2018). Quantitative relationship between corruption and development of the Tunisian stock market. Public and Municipal Finance, Vol.7, No.(2), 39-47. doi:10.21511/pmf.07(2).2018.04

Ibtissem Missaoui; Mohsen Brahmi; Jaleleddine Ben Rajeb. Ownership structure management and its effect on dividend policy in the Tunisian stock exchange enterprises: an empirical study, International Journal of Technology Transfer and Commercialisation, 2022 Vol.19 No.1, pp.83 - 96. DOI: 10.1504/IJTTC.2022.123084

Thank you for the observation. We cite the two suggested papers in the literature review in the first research trend that centers on sustainable practices' impact on supply chain risk and performance. Please see highlighted in green.

4. Methodology:

 * Variables (sustainability, uncertainty, and risk): Control varibles provide more statistical significance when they added.

Thank you for the observation. In established research literature, the addition of control variables enhances statistical significance in analyzing phenomena. Although their inclusion has the potential to elevate statistical significance, we deliberately exclude control variables in our study. This decision is rooted in our belief that the most insightful examination of the core variables (sustainability, uncertainty, and risk) is achieved without the confounding effects introduced by control variables. This approach fosters a focused analysis, revealing direct relationships among the primary variables while minimizing added complexity from controlling for other factors.

 * ( line 233) : ''all daily returns demonstrate a negative bias and display  high values for the Kurtosis statistics, indicating heavy-tailed distortions in the data' : explain this negative bias result found?

Thank you for the observation. This finding suggests that the daily returns consistently tend to be lower than expected. The negative bias could be due to various factors such as market downturns. Additionally, the high Kurtosis values point to frequent extreme values in the data, indicating that the returns exhibit heavier tails in the distribution, implying more instances of significant deviations from the average return. This sentence was added to the paper as an explanation of the results. Please see highlighted in green.

* Dickey-Fuller (ADF) demonstrate that all return series are stationary : provide from your table1 the illustrated result (give the P-value and compare it to the theory of ADF).

* the same thing for the tests : JB  & LBQ.

Thank you for the observation. The p-values for both tests appear in the table are reported within square brackets and are explained in the notes to the table. Please see highlighted in green.

 * Wavelet Analysis : (known as multi-resolution decomposition): Provide Cited reference authors who used this statistic approach.

Thank you for the observation. Wavelet analysis approach has been widely by different authors for different time-frequency analysis that involve different financial assets (Aguiar-Conraria & Soares, 2011; Duan et al., 2021; Jammazi & Reboredo, 2016; Kassouri et al., 2022; Marín-Rodríguez et al., 2023; Pal & Mitra, 2017; Ramsey & Lampart, 1998; Reboredo et al., 2017; Reboredo & Rivera-Castro, 2014; Tien & Hung, 2022). This sentence was added to the paper with the following references.

Aguiar-Conraria, L., & Soares, M. J. (2011). Oil and the macroeconomy: using wavelets to analyze old issues. Empirical Economics, 40(3), 645–655. https://doi.org/10.1007/s00181-010-0371-x

Duan, W., Khurshid, A., Rauf, A., Khan, K., & Calin, A. C. (2021). How geopolitical risk drives exchange rate and oil prices? A wavelet-based analysis. Energy Sources, Part B: Economics, Planning and Policy, 16(9), 861–877. https://doi.org/10.1080/15567249.2021.1965262

Jammazi, R., & Reboredo, J. C. (2016). Dependence and risk management in oil and stock markets. A wavelet-copula analysis. Energy, 107, 866–888. https://doi.org/10.1016/j.energy.2016.02.093

Kassouri, Y., Bilgili, F., & Kuşkaya, S. (2022). A wavelet-based model of world oil shocks interaction with CO<inf>2</inf> emissions in the US. Environmental Science and Policy, 127, 280–292. https://doi.org/10.1016/j.envsci.2021.10.020

Marín-Rodríguez, N. J., González-Ruiz, J. D., & Botero, S. (2023). A Wavelet Analysis of the Dynamic Connectedness among Oil Prices, Green Bonds, and CO2 Emissions. Risks, 11(1), 15. https://doi.org/10.3390/risks11010015

Pal, D., & Mitra, S. K. (2017). Time-frequency contained co-movement of crude oil and world food prices: A wavelet-based analysis. Energy Economics, 62, 230–239. https://doi.org/10.1016/j.eneco.2016.12.020

Ramsey, J. B., & Lampart, C. (1998). Decomposition of economic relationships by timescale using wavelets: Money and income. Macroeconomic Dynamics, 2(1), 49–71. https://doi.org/10.1017/S1365100598006038

Reboredo, J. C., & Rivera-Castro, M. A. (2014). Wavelet-based evidence of the impact of oil prices on stock returns. International Review of Economics & Finance, 29, 145–176. https://doi.org/10.1016/j.iref.2013.05.014

Reboredo, J. C., Rivera-Castro, M. A., & Ugolini, A. (2017). Wavelet-based test of co-movement and causality between oil and renewable energy stock prices. Energy Economics, 61, 241–252. https://doi.org/10.1016/j.eneco.2016.10.015

Rua, A. (2012). Wavelets in economics. Economic Bulletin and Financial Stability Report Articles, 8, 71–79.

Tien, H. T., & Hung, N. T. (2022). Volatility spillover effects between oil and GCC stock markets: a wavelet-based asymmetric dynamic conditional correlation approach. International Journal of Islamic and Middle Eastern Finance and Management. https://doi.org/10.1108/IMEFM-07-2020-0370

* Wavelet Coherence : ??? 2 (?, ?) serves as a scale-specific squared correlation between the two series :  The result of tis test can be arranged in statistical table?

Wavelet Coherence (???^2(?, ?)) provides scale-specific squared correlations between two series. These results are usually presented graphically rather than in a traditional statistical table due to their frequency and time-based nature.

5. Application and Results:

* Unconditional Correlation Analysis: MOVE Index (RMOVE) is negative (-33%), and the Cboe Volatility Index (RVIX) is negative too (-63%) : What are this implication findings to the sustainability and other variables?

Thank you for the observation. The negative correlations between the Sustainability World Index (RW1SGI) and both the ICE BofA MOVE Index (RMOVE) and the Cboe Volatility Index (RVIX), at -33% and -63% respectively, suggest that as RW1SGI (a sustainability index) decreases, market volatility, as represented by RMOVE and RVIX, tends to increase. These findings could imply that shifts in sustainability perceptions or events might coincide with heightened market volatility. Additionally, the positive correlation of 0.32% between the ICE BofA MOVE Index (RMOVE) and the Cboe Volatility Index (RVIX) suggests that there is a tendency for these two volatility indices to move in the same direction. When RMOVE increases, RVIX also tends to increase, and vice versa. This implies a potential alignment between fixed-income market volatility (RMOVE) and stock market volatility (RVIX). This finding could be useful for investors and analysts in understanding how volatility in different market segments might be interconnected or influenced by similar factors. This sentence was added to the paper. Please see highlighted in green.

* Wavelet Power Spectrum : "The Cboe Volatility Index (RVIX) behavior, as depicted in Figure 5b, demonstrates notable volatility during 2020 and the beginning of 2023" : Compare this period (Ukraine/Russia ware)  to Covid pandemic (2019-2021) in term of volatility and inflation VIX index.

The following explanation was added to the paper. Please see highlighted in green.

Thank you for the observation. The Cboe Volatility Index (RVIX) behavior, as depicted in Figure 5b, demonstrates notable volatility during 2020 and the beginning of 2023 across low, medium, and high frequencies, aligning with the previously identified events. Notably, the COVID-19 pandemic, the Russian invasion of Ukraine, and the ensuing European energy crisis triggered post-COVID-19 inflation and impacted the global markets, leading to increased interest rates. Consequently, heightened volatility in the financial markets contributed to an up-surge in both the Volatility and Uncertainty Indexes. Then, both periods experienced heightened volatility. However, it's important to note that the COVID-19 pandemic period (2019-2021) was marked not only by volatility (VIX index) but also by increased inflation, reflecting a broader economic impact compared to the more localized impact of the Ukraine-Russia conflict on volatility.

* Wavelet Coherence Approach : 

''....all the periods analyzed, indicating that in the short- medium- and long- term, the DJ Sustainability World Index (RW1SGI) significantly affected the Cboe Volatility Index (RVIX) negatively'' : Explain this result and the real impact to the financial Market and uncertanity indexes?

Thank you for the observation. The following explanation was added to the paper. Please see highlighted in green.

The wavelet coherence analysis in Figure 6a reveals a consistent pattern of left-up ar-rows across all scales, indicating that the DJ Sustainability World Index (RW1SGI) has a significant negative impact on the Cboe Volatility Index (RVIX) across short, medium, and long-term periods. This suggests that as the sustainability index (RW1SGI) decreases, the volatility index (RVIX) tends to increase, highlighting a potentially inverse relationship between sustainability and market volatility.

In practical terms, this outcome implies that shifts in sustainability factors are associated with changes in market volatility. A decrease in sustainability, which might be related to negative environmental, social, or economic developments, appears to coincide with increased market volatility. This could mean that negative events or factors affecting sustainability might introduce higher uncertainty or perceived risk to the financial market, leading to heightened volatility.

Such insights from the analysis can aid investors and financial analysts in under-standing the interconnected dynamics between sustainability considerations and market uncertainty. It suggests that maintaining or enhancing sustainability practices might con-tribute to stabilizing market conditions and potentially reducing volatility by mitigating sources of uncertainty related to environmental and social factors.

* "the direction of causality changed during different periods and frequencies. 432 From 2014 to 2023, for the intervals of 2014, 2019-2020, 2021, and 2022-2023" : What is the rised period from thoses cited and your macroeconomic interpretation?

Thank you for the observation. The following explanation was added to the paper. Please see highlighted in green.

Notably, the direction of causality changed during different periods and frequencies. Between 2014 and 2023, during intervals like 2014, 2019-2020, 2021, and 2022-2023, a consistent pattern of left-down arrows emerged for the scale range of 4–64. This pattern indicates that, in the short-term, the ICE BofA MOVE Index (RMOVE) exhibited a signifi-cant negative influence on the DJ Sustainability World Index (RW1SGI). Additionally, this negative influence was also apparent in both the short-term and medium-term during 2014.

The periods with the pronounced causal relationship between RMOVE and RW1SGI suggest instances of heightened market turbulence and uncertainty. These periods are tied to significant macroeconomic events that involve geopolitical tensions, financial crises, and environmental concerns. The consistent negative impact on sustainability sentiments during these identified intervals implies that increased market volatility, as represented by RMOVE, coincided with reduced focus on sustainability, potentially due to shifts in investor behavior, market sentiment, or broader economic concerns. These nuanced temporal shifts underscore the intricate relationship between financial market dynamics and sustainability considerations, revealing the impact of external forces on market sentiments and long-term sustainability priorities.

6. Limitation section?

The limitations were included in the conclusions section. Please see highlighted in green.

7. Conclusion and recommendations

Thank you for the observation. Recommendation added. Please see highlighted in green.

8. References:  all new recent cited references in the text (theory section) must be added in this section.

Thank you for the observation. All new references were added both in the citations and in the references of the study. Please see highlighted in green.

Again, thank you very much for your constructive comments. The paper has benefited significantly from your helpful suggestions. We recognize the importance of your contributions at the end of the paper in the acknowledgments. We hope that you find this new version to be suitable for publication.

 

Author Response File: Author Response.pdf

Reviewer 2 Report

The paper is generally well-written. However, I have a few comments before I finally accept the paper. First, the significance of the Time-frequency relationships has to be more explicitly and more comprehensively discussed in the beginning. The whole paper is based on this concept. Second, the sustainability aspect has been poorly introduced in the beginning so please elaborate more precisely on the areas of sustainability the paper is trying to highlight. The method is generally adequate and fits to this type of research and the findings are interesting however there was a poor reflection of the findings on practice. For instance there was almost zero reflection on the BASEL accords concerning Market risk. What are the implications in relation to the procedures mandated by the BASEL accord concerning this? The coverage period of the study makes it worthy of publication as the sampling period spans from 1 January 2014 to 12 July 2023, including 2486 daily observations, which reflects recent findings. More importantly, the application and results section was highly underdeveloped. So please elaborate more on this section showing the implications more explicitly and how the findings contribute to the development of practice. A very significant element that was also poorly presented is the lessons learned. You have mentioned that: "COVID-19 pandemic, making it one of the most volatile 213 periods in recent market history", so what lessons have been learned and should be reflected on your findings. Please elaborate more clearly. 

I think that the English language is overall fine and that the paper is generally clear and understandable. 

Author Response

August 25, 2023

Reviewer 2

Sustainability Journal

 

Re: Manuscript title: Sustainability, uncertainty, and risk: Time-frequency relation-ships

 

Ref.: Manuscript ID: sustainability -2563039

 

Dear Professor:

 

Thank you for your independent review report submitted dated August 8, 2023. We are very grateful for the comments and constructive suggestions raised by you. We have reviewed them all carefully and have revised the manuscript accordingly. Please see our detailed response below:

1. First, the significance of the Time-frequency relationships has to be more explicitly and more comprehensively discussed in the beginning. The whole paper is based on this concept.

Thank you for the observation. The following explanation was added to the paper in the introduction section. Please see highlighted in green.

 

The analysis of time-frequency relationships holds significant value as it enables us to unveil how the interactions between variables evolve over different time frames. This approach provides a dynamic perspective, allowing us to identify patterns and correlations that might be missed in traditional static analyses (Rua, 2012). By examining how relationships change across various frequencies and time intervals, we gain a deeper understanding of complex phenomena, such as the interplay between sustainability, uncertainty, and risk. This nuanced insight is crucial for devising informed strategies, making accurate predictions, and adapting to the ever-changing dynamics of financial markets and economic landscapes.

 

2. Second, the sustainability aspect has been poorly introduced in the beginning so please elaborate more precisely on the areas of sustainability the paper is trying to highlight. The method is generally adequate and fits to this type of research and the findings are interesting however there was a poor reflection of the findings on practice. For instance there was almost zero reflection on the BASEL accords concerning Market risk. What are the implications in relation to the procedures mandated by the BASEL accord concerning this?

Thank you for the observation. The following explanation was added to the paper in the introduction section. Please see highlighted in green. The Dow Jones Sustainability World Index holds substantial importance as a comprehensive measure of sustainability performance across global markets. It assesses companies based on environmental, social, and governance criteria, offering investors a clear view of businesses' commitment to long-term sustainability and responsible practices. This index not only guides investment decisions towards environmentally conscious and socially re-sponsible companies but also encourages corporations to enhance their sustainability ef-forts. In a world increasingly focused on sustainable practices, the Dow Jones Sustainability World Index plays a pivotal role in promoting economic growth that aligns with social and environmental well-being.

 

Furthermore, the discussion section was introduced to encompass the implications of the BASEL accord in the present context of analysis.

 

3. The coverage period of the study makes it worthy of publication as the sampling period spans from 1 January 2014 to 12 July 2023, including 2486 daily observations, which reflects recent findings. More importantly, the application and results section was highly underdeveloped. So please elaborate more on this section showing the implications more explicitly and how the findings contribute to the development of practice.

Thank you for the observation. The implications of the results were improved. Please see highlighted in green.

 

4. A very significant element that was also poorly presented is the lessons learned. You have mentioned that: "COVID-19 pandemic, making it one of the most volatile 213 periods in recent market history", so what lessons have been learned and should be reflected on your findings. Please elaborate more clearly. 

Thank you for the observation. The implications of the results were improved. Please see highlighted in green.

Again, thank you very much for your constructive comments. The paper has benefited significantly from your helpful suggestions. We recognize the importance of your contributions at the end of the paper in the acknowledgments. We hope that you find this new version to be suitable for publication.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Dear Authors

English language ( spelling): revise again from the introduction to the Disscussion section.

 

Minor editting English language ( spelling)

Reviewer 2 Report

The corrections/amendments have been made for the satisfaction of the reviewer. Good luck.