Volatility Persistence and Spillover Effects of Indian Market in the Global Economy: A Pre- and Post-Pandemic Analysis Using VAR-BEKK-GARCH Model

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Paper ID: 3055063 "Volatility Persistence and Spillover Effects of Indian Market in the Global Economy: A pre-and post- pandemic Analysis Using VAR-BEKK-GARCH Model"

 

The paper investigates the impact of the COVID-19 pandemic on stock market volatility and interconnectedness between India and other emerging economies using the VAR-BEKK-GARCH model. The findings suggest that while the Indian market's volatility influenced several other markets before and after the pandemic, the influence of the US market diminished post-pandemic. This study is important as it highlights the lasting effects of the pandemic on global financial markets and provides valuable insights.

 

Additional Comments:

·         Page 2-3: Several references are outdated and should be replaced by more recent papers that are focused on the topic.

·         Page 7-8: The justification for selecting the VAR-BEKK-GARCH model is insufficient. A clear justification is needed along with some explanation on the developments that led to the model used.

·         Throughout the manuscript: There is no mention of performing robustness checks or comparing results with other models to ensure the validity and reliability of the findings.

·         Page 7: Clarify on how non-overlapping trading days were handled.

·         Page 7: The rationale behind selecting specific stock indices (India, Mexico, Russia, New York, China, Brazil) is not well-explained. Also The paper should clearly define what it considers to be "emerging markets" and ensure consistency (New York) in the classification throughout the analysis.

·         Page 11-13: The discussion in its present form, focuses heavily on statistical significance without adequately discussing the practical implications of the findings.

·         Page 14-15: Some conclusions appear overstated given the results presented.  Explain the significant policy implications based on the findings?

 

While the paper addresses an important topic and employs advanced econometric models, it suffers from insufficient justification for model selection, a lack of robustness checks, and unsupported practical implications. Addressing these weaknesses would significantly improve the quality and impact of the study. After addressing these major revisions, further comments can be provided on the analysis and results.

Comments for author File: Comments.docx

Author Response

Comment-1: Page 2-3: Several references are outdated and should be replaced by more recent papers that are focused on the topic.              

Response-1: Old references are removed and latest references added

Comment-2: Page 7-8: The justification for selecting the VAR-BEKK-GARCH model is insufficient. A clear justification is needed along with some explanation on the developments that led to the model used.  

Response-2: Justification provided with an additional table showing the robustness test for the BEKK-GRACH Model in table 1

Comment-3:  Throughout the manuscript: There is no mention of performing robustness checks or comparing results with other models to ensure the validity and reliability of the findings.                                  

Response-3: Robustness checks were done and given in the Table I with proper explanation.

Comment-4:    Page 7: Clarify on how non-overlapping trading days were handled.                                                                              

Response-4: Explanation/justification has been given in line 391-400 for the handling of non-overlapping and missing day data. (Forward filling method adopted for the same)

Comment-5:   Page 7: The rationale behind selecting specific stock indices (India, Mexico, Russia, New York, China, Brazil) is not well-explained. Also, The paper should clearly define what it considers to be "emerging markets" and ensure consistency (New York) in the classification throughout the analysis.                                                                             

Response-5: Justification has been provided in the lines 362-373

Comment-6:  Page 11-13: The discussion in its present form, focuses heavily on statistical significance without adequately discussing the practical implications of the findings.                                                 

Response-6: The discussion section has been improvised by providing practical implications highlighted in blue color in the section 3.2 from line 530 to 607

Comment-7:   Page 14-15: Some conclusions appear overstated given the results presented.  Explain the significant policy implications based on the findings?                                                                             

Response-7: Conclusion section has been improved and re-written and limitation and scope for further study has been added

Note: Revised text has been highlighted in blue colour

Reviewer 2 Report

Comments and Suggestions for Authors

Main Comments

1. Splitting the sample into pre- and post-COVID at 31 Jan 2020 is problematic. There are really three periods that should be considered: pre-, post- and during-. However, it's difficult to determine when the during period should begin and end. The reason this issue is important for your study is that splitting into just two regimes puts all of the effects from the early days of the pandemic in the post period. During this time, significant portions of the global economy were shutting down. Dramatic fiscal and monetary policies were being implemented around the world. Certainly some of these policies had persistent effects, but at some point financial markets calmed down and gradually normalized. How does the 2023- early 2024 period compare with pre-COVID period in your analysis? What about using models with structural breaks?

2. I do not understand how you generate Table III and Figure 3 with the equations in Section 2.1.1. In fact, these equations are not even VARs, but rather five separate linear equations.

Minor Comments

Why write MEXICO in all caps?

Mexico is also misspelled as MAXICO in Figure 3.

Comments on the Quality of English Language

The English is generally fine, but could use some light editing in places.

Author Response

Comment-1: Splitting the sample into pre- and post-COVID at 31 Jan 2020 is problematic. There are really three periods that should be considered: pre-, post- and during-. However, it's difficult to determine when the during period should begin and end. The reason this issue is important for your study is that splitting into just two regimes puts all of the effects from the early days of the pandemic in the post period. During this time, significant portions of the global economy were shutting down. Dramatic fiscal and monetary policies were being implemented around the world. Certainly some of these policies had persistent effects, but at some point financial markets calmed down and gradually normalized. How does the 2023- early 2024 period compare with pre-COVID period in your analysis? What about using models with structural breaks?

Response-1: The inclusion of early 2024 period in the analysis is based on the authors assumption that even though the initial shock due to pandemic has subsided, markets continue to experience fluctuations due to the pandemic's economic aftermath, changes in monetary policies, inflation concerns, adverse and favourable economic effect on various business around the globe and geopolitical tensions.

Comment-2: I do not understand how you generate Table III and Figure 3 with the equations in Section 2.1.1. In fact, these equations are not even VARs, but rather five separate linear equations.

Response-2: We have revised the VAR equation in lines 253-268 and also the Table-IV (new) and Figure IV (new) for the impulse-response curve  

Comment-3 : Why write MEXICO in all caps? Mexico is also misspelled as MAXICO in Figure 3. 

Response-3: Corrections done to the mistake highlighted by the reviewer.

Comment-4 :The English is generally fine, but could use some light editing in places.

Response-4:  English has been improved

Reviewer 3 Report

Comments and Suggestions for Authors

The paper titled "Volatility Persistence and Spillover Effects of Indian Market in the Global Economy: A pre-and post-pandemic Analysis Using VAR-BEKK-GARCH Model" by Narayana Maharana, Ashok Kumar Panigrahi, and Suman Kalyan Chaudhury aims to analyze the impact of the COVID-19 pandemic on stock market volatility and interconnectedness between India and other emerging economies using data from 2016 to 2024. The authors employ VAR and BEKK-GARCH models to study volatility persistence and transmission patterns.

However, there are several concerns regarding the rigor and clarity of the econometric modeling presented in the paper:

1. Sloppy Representation of Econometric Models:

   • In lines 256-260, the factor loadings on the right-hand side are the same for each regression model, as are the residual terms. This uniformity lacks justification and suggests a lack of attention to the specific dynamics of each market pair being modeled.

2. Lack of Model Justification:

   • There is no clear justification for the chosen models. The authors do not explain why the VAR-BEKK-GARCH model is appropriate for this study, nor do they discuss alternative models that were considered and why they were rejected.

3. Absence of Model Backtesting:

   • The model is not backtested to check how parsimonious it is. Backtesting is crucial for verifying the predictive power and robustness of econometric models, especially in a study aiming to inform policy and investment decisions.

4. Standardization of Residuals:

   • In line 298, the residuals are said to be standardized. This likely means that the authors assume the residuals to be independently and identically distributed (i.i.d.) standard normal vectors. However, it is probable that the sample residuals fail normality tests, exhibiting non-Gaussian distribution and possibly long-range dependence. This assumption should be tested and addressed, as it significantly impacts the validity of the model's results.

Given these issues, the paper needs a major revision to be acceptable for publication in the Journal of Risk and Financial Management (JRFM). Specifically, the authors should:

• Provide a thorough justification for the chosen models.
• Ensure that the econometric models are clearly and correctly specified.
• Conduct and report backtesting results to demonstrate the model's reliability and robustness.
• Test the residuals for normality and independence, and adjust the model accordingly if these assumptions are violated.

These revisions are necessary to enhance the credibility and rigor of the study, making it a valuable contribution to the literature on stock market volatility and spillover effects in the context of the COVID-19 pandemic.

Author Response

Comment-1: Sloppy Representation of Econometric Models: In lines 256-260, the factor loadings on the right-hand side are the same for each regression model, as are the residual terms. This uniformity lacks justification and suggests a lack of attention to the specific dynamics of each market pair being modeled. .

Response-1: the VAR equation given in those lines has been corrected and you can find the revised one in lines 252-268. And the corresponding table IV and figure IV has also been revised

Comment-2: Lack of Model Justification: There is no clear justification for the chosen models. The authors do not explain why the VAR-BEKK-GARCH model is appropriate for this study, nor do they discuss alternative models that were considered and why they were rejected.

Response-2: Justification provided with an additional table showing the robustness test for the BEKK-GRACH Model in table 1

Comment-3: Absence of Model Backtesting: The model is not backtested to check how parsimonious it is. Backtesting is crucial for verifying the predictive power and robustness of econometric models, especially in a study aiming to inform policy and investment decisions.

Response-3: The suitability of the VEKK-GARCH model with all backtesting has been given and discussed in lines 345-360

Comment-4: Standardization of Residuals: In line 298, the residuals are said to be standardized. This likely means that the authors assume the residuals to be independently and identically distributed (i.i.d.) standard normal vectors. However, it is probable that the sample residuals fail normality tests, exhibiting non-Gaussian distribution and possibly long-range dependence. This assumption should be tested and addressed, as it significantly impacts the validity of the model's results.

Response-4: We are extremely sorry for omitting the QMLE function which is the most important element of the given econometric model. We are thankful to the reviewer for such a very in-depth review and valuable suggestion. We have corrected ourself and incorporated the QMLE equation which addresses the concern raised by the reviewer in lines 325-342

Comment-5: Provide a thorough justification for the chosen models.

Response-5: Justification provided

Comment-6: Ensure that the econometric models are clearly and correctly specified.

Response-6: We have addressed this issue meticulously

Comment-7: Conduct and report back testing results to demonstrate the model's reliability and robustness. Robustness check has been done and presented in table I. Test the residuals for normality and independence, and adjust the model accordingly if these assumptions are violated.

Response-7:  Since for some variables the residuals follow normalcy and for some it does not, hence, we assumed that the residuals are not following normal distribution and addressed the issues with considering the QMLE function which has been presented in the lines 325-342

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Thank you for addressing all of my comments. I wish you all the best with your paper.

Reviewer 3 Report

Comments and Suggestions for Authors

The paper by Maharana, Panigrahi, and Chaudhury provides an insightful analysis of how the COVID-19 pandemic has impacted stock market volatility and interconnectedness between India and other global economies. The study spans data from 2016 to 2024, utilizing sophisticated econometric models (VAR-BEKK-GARCH) to evaluate volatility transmission before and after the pandemic.

Strengths:

1. Comprehensive Analysis: The study comprehensively covers both pre- and post-pandemic periods, providing a detailed understanding of volatility dynamics.
2. Use of Advanced Models: Employing the VAR-BEKK-GARCH model adds robustness to the analysis, effectively capturing the nuances of volatility spillovers.
3. Relevance: The findings are highly relevant, given the ongoing interest in understanding the pandemic's impact on financial markets globally.
4. Implications for Policy and Practice: The paper offers valuable insights for policymakers and investors, emphasizing the importance of robust risk management strategies in a post-pandemic world.

5. Conclusion: Overall, the paper makes a significant contribution to the literature on financial market volatility and interconnections in the context of the COVID-19 pandemic. The analysis is well-executed, and the conclusions are well-supported by the data.

   Recommendation: I recommend accepting the revised version of the paper for publication in the Journal of Risk and Financial Management (JRFM).