Climate Risks and Stock Market Volatility over a Century in an Emerging Market Economy: The Case of South Africa

Round 1

Reviewer 1 Report

It is necessary to ask yourself the question for whom the article is primarily intended - whether for those interested in mathematical methods or for those interested in the factors affecting changes in the stock market.

I do not have adequate education in mathematics and statistics, so I cannot comment on newly proposed model-free prediction methods. Regarding the area of influencing factors, I would like to comment:

- the influence of temperature anomaly and/or its volatility on the forecast of stock returns volatility is very interesting. It belongs to the field of rare disaster events, to which the authors refer in a relatively extensive overview of the literature. I believe that not all readers will be willing to study these links. A brief description of these linkages would be beneficial, particularly indicating whether they are the results of empirical investigations or whether there is some theoretical basis.

From a formal point of view, the article is clear, all literary references are cited in the text.

Author Response

Response to reviewer 1:

Comment 1: the influence of temperature anomaly and/or its volatility on the forecast of stock returns volatility is very interesting. It belongs to the field of rare disaster events, to which the authors refer in a relatively extensive overview of the literature. I believe that not all readers will be willing to study these links. A brief description of these linkages would be beneficial, particularly indicating whether they are the results of empirical investigations or whether there is some theoretical basis.

Response: We thank the referee for this comment. It must be realized that, traditionally, disaster events are generally captured by cumulative declines in output and/or consumption of at least 10% over one or more years (see, Ćorić (2021), and Ćorić and Šimić (2021) for detailed discussions in this regard). Given this, a major obstacle to full-fledged empirical verification of rare disaster models is that individual countries rarely face such major disasters, resulting in a small sample problem inherent in the use of actual rare disasters, which, in turn, explains why earlier researchers studying the implications of rare disasters for asset pricing have relied on theoretical models calibrated on rare-disaster-risk probabilities derived from historical cross-country evidence of major declines in output and/or consumption. The physical risks associated with climate change due to rising temperatures and its volatility can serve as a solution to the small sample problem that would make the empirical estimation of such models feasible, especially when we look at long spans of data (Donadelli et al., 2017, 2021a, b, 2022), which is the approach that we undertake. In other words, temperature changes and its volatility serve as an empirical measurable proxy of the theoretical concept of rare disaster risks, while simultaneously also allowing us to study the effect of climate change on asset prices by associating global warming as rare disaster events. We have now made this issue clear in the 4^th paragraph of the introduction of the paper, by indicating that use of temperature and its volatility as rare disaster risks have both theoretical and empirical foundations.

Reviewer 2 Report

1) Abstract - There are no recommendations

2) Keywords. Are significant chosen according to the topic. There are no recommendations

3) Structure of the paper:

a) In the Introduction section:

- it is inappropriate to start a sentence with the brackets. Please use the name of the autor or the first name of the first author as much as you can in the paper, as well.

- please use the hyphen for this reference “of [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38],”. Even if, it should be more useful for readers to present in detail the contribution of these works

- I recommend splitting the Introduction section into two sections. The first is “Introduction” in which you present the problem statement, the main contributions of the paper, and how the paper is organized in sections (Section 2 describes…. Section 3 presents).  The second is the “Literature review”

b) In the Conclusion section:

- please mention the limitation of your research

Please revise the writing of:

- “returns”. The correct form is “return” (Line 8)

- “Moreover, in another”. The correct form is “Moreover, another” (Line 21)

- “events does not resolve, but rather increases”. The correct form is “events do not resolve but rather increase” (Line 26)

- “century, but also”. The correct form is “century but also” (Line 49)

- “commodities like, coal, chrome”. The correct form is “commodities such as coal, chrome” (Line 50)

- “measure for the vulnerability”. The correct form is “measure of the vulnerability” (Line 74)

- “policy makers”. The correct form is “policymakers” (Line 75)

- “macroeconomic”. The correct form is “macroeconomics” (Line 94)

- “roles of a consumption”. The correct form is “roles of consumption” (Line 98)

- “prices, because it should be”. The correct form is “prices because should be” (Line 101)

- “risk averse”. The correct form is “risk-averse” (Line 103)

- “recently”. The correct form is “recent” (Line 107)

- “in an analogous way”. The correct form is “analogously” (Line 193)

- “so as to”. The correct form is “to” (Line 199)

- “In order define a”. The correct form is “In order tot define a” (Line 213)

- “Our aim”. The correct form is “We aim” (Line 233)

- “three time”. The correct form is “three-time” (Line 250)

- “to represent the volatility”. The correct form is “represent the volatility” (Line 298)

- “1, 3 and 6 steps-ahead”. The correct form is “1, 3, and 6 steps-ahead” (Line 305)

- “to improve”. The correct form is “in improving” (Line 315)

- “an important”. The correct form is “is an important” (Line 333)

Author Response

Response to reviewer 2:

Comment 1: In the Introduction section:

- it is inappropriate to start a sentence with the brackets. Please use the name of the author or the first name of the first author as much as you can in the paper, as well.

- please use the hyphen for this reference “of [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38],”. Even if, it should be more useful for readers to present in detail the contribution of these works

- I recommend splitting the Introduction section into two sections. The first is “Introduction” in which you present the problem statement, the main contributions of the paper, and how the paper is organized in sections (Section 2 describes…. Section 3 presents).  The second is the “Literature review”

Response: Thank you for your suggestions. (1) We have revised the first sentence; (2) For the reference style, we applied the latex format provided by the journal, which requires citing papers as the number of citation; (3) Thank you for pointing out this mistake. We have adjusted the reference in the text according to the requirement. (4) We have decomposed the introductory section into two subsections: one is background and the outline and the other one is the literature review of GARCH methods, specifically on South Africa, and the NoVaS method in general. The objective of this section is to basically highlight that a lot of work has been done using the GARCH approach to forecast South African stock returns volatility, using a wide array of predictors, but nothing on climate risks. Doing a detailed analysis of what each of these studies have done is beyond the scope of our paper (the interested reader is referred to the cited studies) as this would divert a reader's attention away from the main focus of our work, which is relating climate risks to stock returns volatility for an emerging market country for the first time using both GARCH and NoVaS approaches.

Comment 2: In the Conclusion section:

- please mention the limitation of your research

Response: Thank you for your suggestion! In the concluding section, we mention that our paper could be extended in future research to analyze other emerging market economics, as well as the currency markets of South Africa. In addition, we also claim that the success of the NoVaS method requires good transformation result. In order to further improve the methods based on the model-free prediction principle, one could resort to other state-of-the-art techniques, e.g., deep neural networks.

Comment 3: Please revise the writing of below sentences:

- “returns”. The correct form is “return” (Line 8)

- “Moreover, in another”. The correct form is “Moreover, another” (Line 21)

- “events does not resolve, but rather increases”. The correct form is “events do not resolve but rather increase” (Line 26)

- “century, but also”. The correct form is “century but also” (Line 49)

- “commodities like, coal, chrome”. The correct form is “commodities such as coal, chrome” (Line 50)

- “measure for the vulnerability”. The correct form is “measure of the vulnerability” (Line 74)

- “policy makers”. The correct form is “policymakers” (Line 75)

- “macroeconomic”. The correct form is “macroeconomics” (Line 94)

- “roles of a consumption”. The correct form is “roles of consumption” (Line 98)

- “prices, because it should be”. The correct form is “prices because should be” (Line 101)

- “risk averse”. The correct form is “risk-averse” (Line 103)

- “recently”. The correct form is “recent” (Line 107)

- “in an analogous way”. The correct form is “analogously” (Line 193)

- “so as to”. The correct form is “to” (Line 199)

- “In order define a”. The correct form is “In order tot define a” (Line 213)

- “Our aim”. The correct form is “We aim” (Line 233)

- “three time”. The correct form is “three-time” (Line 250)

- “to represent the volatility”. The correct form is “represent the volatility” (Line 298)

- “1, 3 and 6 steps-ahead”. The correct form is “1, 3, and 6 steps-ahead” (Line 305)

- “to improve”. The correct form is “in improving” (Line 315)

- “an important”. The correct form is “is an important” (Line 333)

Response: Thank you for your careful reading. We have revised these sentences carefully.

Reviewer 3 Report

1.     The introduction and Literature Review should be improved. The author should add some of the latest studies on climate risk and stock market volatility ( from 2020 to 2023).

2.     The author should explain in more detail the facts and figures.

3.     The reference in the text should be adjusted according to the journal requirement. (e.g., [11 – 15] and [23 –38].

4.     The DOI number should be added to the references.

5.     The author should compare the current results with the previous studies. Try to highlight the contribution in terms of (data, methodology, and results). 

6.     The conclusion should be improved.

Need to improve the grammatical errors.

Author Response

Response to reviewer 3:

 Comment 1: the introduction and Literature Review should be improved. The author should add some of the latest studies on climate risk and stock market volatility ( from 2020 to 2023).

Response: We thank the referee for this comment. A new brief literature-review segment has been added to the revised paper. Note that we have indeed cited all relevant papers relating to climate risks and stock markets post 2020, and some important ones before that too. In fact, the only study on stock market volatility and climate risks is that by Bonato et al. 2023, which we have indeed cited in the paper, but this paper was done on US state-level stock market. The other paper relating to exchange rate volatility has also been cited. Actually, climate risks have been primarily related to commodity markets, when it comest to volatility, with us citing relevant papers. Indeed, the recent research on climate risks and financial markets are limited to the first moment and design of optimal portfolios to hedge climate risks, and virtually very little exists on second moment impacts.

Comment 2: The author should explain in more detail the facts and figures.

Response: Thank you for your suggestions, though we are not sure whetherthe referee is referring to Figure 1 or in general. We believe all details about the sources of facts and figures have been well-referenced. We have added more details to describe Figure 1 in more depth (that is, the figure that shows the volatility-clustering phenomenon of the time series.

Comment 3: The reference in the text should be adjusted according to the journal requirement. (e.g., [11 – 15] and [23 –38].

Response: Thank you for pointing out this formatting issue. We have adjusted the references in the text according to the journal requirements.

Comment 4: The DOI number should be added to the references.

Response: Thank you for this comment. Please note that we applied the latex template provided by the journal. It does not require addition of the DOI number. 

Comment 5: The author should compare the current results with the previous studies. Try to highlight the contribution in terms of (data, methodology, and results). 

Response: Thank you for this comment. In our paper, we analyze whether climate risks help to forecast the South African stock returns volatility. Ours is a novel approach to do volatility forecasting. In a classical way, such prediction can be performed with the GARCHX method. In order to avoid the model assumption implied by the GARCHX model, we also extend a model-free prediction method, namely the NoVaS method, such that it can be used to do prediction with exogenous variables.

In summary, for the data aspect, we apply a dataset that covers more than a century to measure climate risks by studying temperature anomalies and/or their volatility. For the methodology aspect, we extend the current NoVaS method to cover the potential exogenous variables. For the results, we show that the NoVaS models that include climate information achieve a stronger improvement of forecast accuracy than GARCH-type models.

These findings are mentioned in the Conclusion section. We have also related our work to the limited number of earlier studies on stock returns volatility and climate risks in the last paragraph of the empirical results segment.

Comment 6: The conclusion should be improved.

Response: Thank you for this suggestion. As required by one of the other reviewers, we have add a discussion of the limitations of our research the Conclusion section. In short, the success the of the NoVaS method depends on a satisfactory transformation result. In order to fully make use of the power of model-free prediction idea, an interesting avenue for future research is to consider other state-of-the-art technique, such as deep neural networks.

Comment 7: Need to improve the grammatical errors.

Response: Thank you for reading our paper carefully. We have revised our paper following your suggestion. Also, we have revised our paper in line with the comments we received from Reviewer #2.

Reviewer 4 Report

(1) How temperature anomaly or its volatility affects the accuracy of stock return volatility prediction is studied from a novel perspective.

(2) A new model-free prediction method (GARCHX-NoVaS) is adopted, and the prediction accuracy of multiple models is compared (Garch-Garch-Novas - GARCHX-NoVaS).

Author Response

Response to reviewer 4:

Comment 1: How temperature anomaly or its volatility affects the accuracy of stock return volatility prediction is studied from a novel perspective.

Comment 2: A new model-free prediction method (GARCHX-NoVaS) is adopted, and the prediction accuracy of multiple models is compared (Garch-Garch-Novas - GARCHX-NoVaS).

Response: We thank the reviewer for the very positive evaluation of our paper.

Reviewer 5 Report

Climate Risks and Stock Market Volatility Over a Century in an Emerging Market Economy: The Case of South Africa

This is an interesting paper on a timely relevant topic. The Paper analyze temperature anomalies and their effects on the South African Stock Market. It imploys on GARCH and GARCHX models and compare them with GARCH-NoVaS and GARCHX-NoVaS and in so doing develop a benchmark study. The result of the study is interesting as it informs temperature anomaly volatility. The implications of the result, however, could have been better explained.

I have three minor concerns that I advise the authors to address. First, I recommend to better explain why temperature anomaly is your focal point in aggregating climate related risk to finance. Second, my concern is the “role played by oil log-returns, the ratio of the gold-to-silver prices” (p. 7) in which I am uncertain about the “global market price variations” in relation to “local temperature anomalies”. This could have been explained better and may be a limitation of the study. In addition, does the “proxy of heat” and relatedly the theory of temperature volatility imply that you expect gold prices to rise during heat waves etc. I suggest the authors explain a bit on this and relatedly spell out their hypothesis in pointing to the Oil log-returns” as I would suspect agriculture, water utilities to be a better proxy etc. Relatedly I advise the authors to resamine the following issue and spell out my concern with the following: There is a local-temperature event (regional temperature anomaly) with – regional or global price effects. Please explain the reasoning or possible limitations. Third, definition of climate risk remains uncertain as well as the embedded climate temperature anomaly (DTY/DYTA). What temperature should be surpassed before it is an anomaly (mean.div, standard.div etc.) This should be explained. Se more comments and details below. Otherwise I congratulate the authors on an informative and interesting piece. 

Abstract

The abstract is well written and well structured. It captures the essence of the paper as well as describe the method and aim in a straightforward form. I would though, recommend that you explain your finding in a more precise manner than in general terms. 

Introduction

“In this model, agents decide on whether and how 23

to prepare for different future states of the world by collecting information, but they also 24

optimally ignore events that are sufficiently unlikely, implying that the realization of such 25

events does not resolve, but rather increases uncertainty (p.2).”

I recommend you explain why temperature anomaly is your focal point in aggregating climate related risk to finance. Why is it a good proxy of a theoretical concept? (p. 2)

Is the below a proxy of heat – or some of the most vulnerable products related to heat/temperature anomaly?  Please do explain. This should be sorted or argued, as I would expect agricultural products for instance being more vulnerable.

“In addition, South Africa 49

is one of the largest exporters of strategic commodities like, coal, chrome, diamond, gold, 50

ilmenite, iron ore, manganese, palladium, platinum, rutile, vanadium, vermiculite, and 51

zirconium.” (p.2)

In addition does the “proxy of heat” and relatedly the theory of temperature volatility imply you expect gold prices to rise during heat waves etc. There is a local-temperature event with – regional or global price effects. Please explain the reasoning/hypothesis that is tested:

“Gold, in turn, serves the 97

dual roles of a consumption good as jewelry, and investors regard it as a “safe haven”, 98

i.e., investors consider it valuable in times of severe financial turmoil. In contrast, silver 99

is a precious metal with similar uses as gold in consumption, but lacks the status of 100

a “safe haven”.” (P. 3)

“Motivated by the superior 112

performance of the newly developed model-free GARCH-NoVaS model, [57] extended 113

this framework to a model that renders it possible to incorporate exogenous predictors, 114

which we then use to study the role of climate risks, as captured by changes in temperature 115

anomaly and/or its volatility, over and above oil returns and the gold-to-silver price ratio, 116

in forecasting stock returns volatility of South Africa.” (p.3).

Definiition of climate risk remain uncertain as well as the embedded climate temperature anomaly data 

“The temperature anomaly (relative to a historical mean over 1991-2020) data for South 240

Africa, upon specifying its coordinates, i.e., stretching latitudinally from 22 S to 35 S and 241

longitudinally from 17  E to 33 E, is available from the National Oceanic and Atmospheric 242

Administration (NOAA)” (p.6). When is a temperature event and anomaly (DTA/DYTA)?- 

Method and Data

Sufficiently described, and the benchmark modelling is well established. My concern is the “role played by oil log-returns, the ratio of the gold-to-silver prices” (p. 7) in which I am uncern about the “global market price variations” in relation to “local temperature anomalies”. This could have been explained better and may be a limitation of the study.

Minor issues

“Eyeballing Figure 1, a volatility clustering phenomenon is quite obvious.” (p.7). Please argue. 

Language

I am not an English native speaker, but I feel the language somewhere needs proof. See e.g. comma, dots here:  “Moreover, in another recent contribution, [6], builds 21

on the literature on inattention to develop a model in which rare disaster risk enhances uncertainty, as well as its, persistence.”(p.2)

“Our aim is to predict the volatility of the Johannesburg Stock Exchange (JSE) All Share 233

Index (ALSI), i.e., JSE-ALSI, with the raw data of the index obtained from Global Financial 234

Data (GFD)I would recommend a language check.” (p. 6)

Charles taylors work might be relevant.

Author Response

Response to reviewer 5:

Comment 1: The abstract is well written and well structured. It captures the essence of the paper as well as describe the method and aim in a straightforward form. I would though, recommend that you explain your finding in a more precise manner than in general terms. 

Response: Thank you for this suggestion! We now write: “Moreover, the novel model-free prediction method can incorporate such exogenous information better than the classical GARCH approach, as revealed by the squared prediction errors. More importantly, the forecast comparison test reveals that the advantage of applying exogenous information related to climate risks in prediction of the South African stock returns volatility is significant over a century of monthly data (1910:02--2023:02).”

Comment 2: I recommend you explain why temperature anomaly is your focal point in aggregating climate related risk to finance. Why is it a good proxy of a theoretical concept? (p. 2).

Is the below a proxy of heat – or some of the most vulnerable products related to heat/temperature anomaly?  Please do explain. This should be sorted or argued, as I would expect agricultural products for instance being more vulnerable.

Response: We thank the referee for this insightful comment. Besides the first two paragraphs of the introduction, please refer to the fourth paragraph of the introductory section, where we discuss how climate risks can serve as a proxy for rare disaster events and how it relates to stock market volatility. But indeed, the referee is right climate risks do impact agricultural commodities as well, and we now discuss briefly this literature too in the literature review segment.

Comment 3: “In addition, South Africa  is one of the largest exporters of strategic commodities like, coal, chrome, diamond, gold, ilmenite, iron ore, manganese, palladium, platinum, rutile, vanadium, vermiculite, and  zirconium.” (p.2)

In addition does the “proxy of heat” and relatedly the theory of temperature volatility imply you expect gold prices to rise during heat waves etc? There is a local-temperature event with – regional or global price effects. Please explain the reasoning/hypothesis that is tested

Response: We thank the referee for this comment. Note that the objective of our research is to forecast stock returns volatility of South Africa, as outlined in paragraph 3 of the introduction, utilizing the role of climate risks, i.e., changes in temperature anomaly and its volatility using GARCH and NoVaS approaches. The focus is not on commodity markets volatility prediction based on climate risks, but stock market volatility of South Africa, based on its local temperature anomaly changes and its volatility.Commodity prices are determined globally and hence would be affected by global climate risks associated with both exporters and importers. When we said: “In addition, South Africa  is one of the largest exporters of strategic commodities like, coal, chrome, diamond, gold, ilmenite, iron ore, manganese, palladium, platinum, rutile, vanadium, vermiculite, and  zirconium.” (p.2), the idea was to motivate why studying the South African stock market is important, given that it is a mining country which is driven by fossil fuels which are going to, in turn, bring about climate change due to rise in temperature and its volatility. These climate risks are likely to feed into the equity market volatility, i.e., its risk. In other words, we wanted to highlight the risk posed by climate change in South Africa is well-motivated with it being involved in many mining industries. We are not really relating fluctuations of climate risks to metal price fluctuations. But indeed, with the economy being so reliant on metals, if climate risks do impact the way mining is done due to transition to a low carbon economy, this can also indirectly impact the equity market as the overall economy is affected. We have noted this in a footnote corresponding to paragraph 5. 

Comment 4: “Motivated by the superior 112

performance of the newly developed model-free GARCH-NoVaS model, [57] extended 113

this framework to a model that renders it possible to incorporate exogenous predictors, 114

which we then use to study the role of climate risks, as captured by changes in temperature 115

anomaly and/or its volatility, over and above oil returns and the gold-to-silver price ratio, 116

in forecasting stock returns volatility of South Africa.” (p.3).

Definition of climate risk remain uncertain as well as the embedded climate temperature anomaly data?

Response: Thank you for this comment, but we are not exactly sure why the definition of climate risks and temperature anomaly is unclear. Please refer to paragraph 3, which clearly indicates why and how climate risks is defined by considering changes in temperature anomaly and its volatility. Temperature anomaly is defined in the data segment, but we now highlight it in paragraph 3 as well.

Comment 5: Method and Data: Sufficiently described, and the benchmark modelling is well established. My concern is the “role played by oil log-returns, the ratio of the gold-to-silver prices” (p. 7) in which I am uncerntain about the “global market price variations” in relation to “local temperature anomalies”. This could have been explained better and may be a limitation of the study.

Response: We thank the referee for this comment. We had to find an innovative way of measuring the state of fundamental and sentiment to avoid omitted variable bias, given that there is no local data available on these two variables for such a long period of time. In this regard, we follow the work of Salisu and Gupta (2022) to proxy oil price returns as capturing the fundamental information of macroeconomic variables, as it is well known that oil price returns impact the domestic macroeconomic indicators of any economy, even though it is a global shock. While sentiment, again based on a global metric, of gold-silver prices ratio is likely to reflect overall sentiment of all economies, as it is well-known that during periods of crises people would want to invest more in a safe haven like gold, which will push its price relative to silver higher. Indeed, these are not perfect proxies but using them is the best that can be done in the current context of dealing with historical data, which is of paramount importance to look at since disaster risks associated with climate change take time to evolve and therefore long data samples are desired. We do not consider this to be a limitation of our work, but it is indeed an empirical issue that has been dealt with in an imperfect, but best possible, manner under the circumstances in dealing with historical data and avoiding the issue of omitted variable bias. We have made this clear in paragraph 8, but it must be noted that climate risk on its own is already likely to include leading information of many domestic macroeconomic variables which drive stock volatility, as it is an aggregate risk for the whole macroeconomy (see paragraph 7).

Comment 6: “Eyeballing Figure 1, a volatility clustering phenomenon is quite obvious.” (p.7). Please argue. 

Response: Thank you for pointing this out. We have added more details to explain this claim.

Comment 7: I am not an English native speaker, but I feel the language somewhere needs proof. See e.g. comma, dots here:  “Moreover, in another recent contribution, [6], builds 21

on the literature on inattention to develop a model in which rare disaster risk enhances uncertainty, as well as its, persistence.”(p.2)

“Our aim is to predict the volatility of the Johannesburg Stock Exchange (JSE) All Share 233

Index (ALSI), i.e., JSE-ALSI, with the raw data of the index obtained from Global Financial 234

Data (GFD)I would recommend a language check.” (p. 6)

Response: Thank you for reading our paper carefully. We have corrected the mistakes and went through the whole draft again carefully.