The Neuroscience of Risk Perception in Financial Markets: How Climate Policy Uncertainty Affects Investor Cognition in India and the USA

Abstract

Background

Climate related risks are increasingly affecting financial markets, most importantly, policy risk regarding climate regulation. Not only do such risks impact market dynamics, but they can also have an impact on investor behaviour as cognitive and emotional reactions to risk. Understanding the interplay between climate policy and market volatility is essential for both economic forecasting and behavioural finance.

Purpose

This study aims to examine the dynamic relationship between climate policy uncertainty and the volatility of major stock market indices viz. Nifty 50 and Sensex in India, and Nasdaq and Dow Jones in the USA and exploring potential neurobehavioural responses of investors to such uncertainty.

Methods

This research aims to determine the dynamic relationship between climate policy uncertainty, Indian benchmark indices Nifty 50 and Sensex, and USA stock market indices Nasdaq and Dow Jones for the monthly data from 1st April 2010 to 31st March 2024. The article has adopted Diebold and Yilmaz’s connectedness framework and WC approach for data analysis. The analysis is interpreted through the lens of neuroeconomics, considering how climate policy uncertainty may influence cognitive risk processing in financial decision-making.

Results

Findings show that indices such as Sensex, Nasdaq and Dow Jones are more responsive to climate policy uncertainty compared with others. These trends suggest that the reactions of global investors are not only strategic but also subject to psychological tension and risk perception mechanisms.

Conclusion

Uncertainty in climate policy exerts a notable influence on stock market volatility with far-reaching implications extending beyond the classical economic indicators to encompass investor cognition and neurobehavioural reactions. The outcome of the current research underscores incorporating neuroscience-informed methods into financial decision-making, providing significant feedback for investors and policymakers regarding risk management, portfolio maximisation and interpreting behavioural responses under environmental uncertainty.

Keywords

Climate policy uncertainty neuroeconomics investor behaviour Dow Jones Nasdaq Nifty 50 wavelet coherence approach

Introduction

The world’s climate is continuously changing.¹ The issue of climate change is one of the most urgent threats faced by economies across the globe, and it is also the focus of all governments.^{2, 3} Emission of greenhouse gases, burning of coal, oil and gas, and deforestation are some prominent reasons for the rise in the earth’s temperature, which leads to the disruptions of its climate system. Additionally, hurricanes, landslides and rising sea levels are some of the consequences of climate change. Compared with 1981–2010, 48% of the world’s land area experienced drought, 151 million more people would be experiencing food insecurity as a result of such extreme weather events, 60% of lands were affected by extreme rainfall last year, which triggered floods and increased the risk of water contamination or infectious diseases, and there was a loss of $835 billion in potential income due to extreme heat in the year 2023.⁴ Climate change is transmitted to the financial system through the impact on the real economy, so governments and financial institutions are becoming more conscious of the necessity of including environmental and climatic risks in the assessment of risk in the financial system.⁵ Events related to climate change may cause immediate direct and indirect losses, which may then negatively affect company operation.⁶ Hence, climate policy uncertainty (hereafter, CPU) has a major impact on the economies across the globe. The main objective of this study is to assess the dynamic interconnectedness and direction of impact among the CPU and Nifty 50, Sensex, Dow Jones and Nasdaq. The UN Framework Convention on Climate Change, which was formulated in 1992 during the ‘Earth Summit’ in Rio de Janeiro, Brazil, with the goal of lowering greenhouse gas emissions, is the first of many important decisions being made worldwide to combat climate change. Since then, a series of landmark decisions were taken in the Kyoto Protocol (1997), the Copenhagen collapse (2009), the Paris landmark deal (2015), Greta Thunberg (2018), the biodiversity deal (2022), the ‘beginning of the end’ for fossil fuels (2023) and the UN climate change conference (COP29) to combat the disruptions of the climate system.⁷

Several studies have investigated the CPU and stock market volatility, but this is the first study to deal with the new dataset and employ the technique of Diebold and Yilmaz’s (2009) connectedness and wavelet coherence (WC) approach for Indian markets. Additionally, this article adds to the literature on pairwise network connectedness among the CPU and Nifty 50, Sensex, Dow Jones and Nasdaq.⁸ Connectedness methodology and WC were utilised because Diebold and Yilmaz’s connectedness methodology allows testing for spillover effects and interconnectedness among different financial variables, and hence is best suited for the measurement of CPU effect on stock market volatility across different indices. The WC approach, allows testing of time-varying relationships over a set of frequencies and time horizons, hence being particularly appropriate to examine the short- and long-run CPU and stock indices’ co-movements that are dynamic in nature.

The study covers the monthly data of CPU, Nifty 50, Sensex, Nasdaq and Dow Jones from 1st April 2010 to 31st March 2024, which constitutes 168 observations. Data on CPU were collected from https://www.policyuncertainty.com and www.yahoofinance.com and are used for Nifty 50, Sensex, Nasdaq and Dow Jones monthly data. The selected timeframe is pertinent in the sense that it incorporates some of the most critical global climate policy milestones, such as the Paris Agreement (2015), Greta Thunberg’s global movement (2018) and the UN Climate Change Conferences through COP28 (2023). These events have produced large impacts on the policy advice, creating variation in CPU, which plays a key role in affecting the relationship between policy shifts and market dynamics. With the sample period covering 10 years, it has 168 monthly observations for each variable, providing statistical strength to the use of advanced methods such as Diebold and Yilmaz’s Connectedness method and WC analysis. Being an extremely large dataset, it ensures that findings in the study are not based on short-term trends alone but instead reflect more long-term dynamic relationships.

Review of Literature

The main aim of a review of literature is to understand the tools, techniques and methodologies used in previous studies and identify the gaps that exist in previous studies. This study is novel in the sense of analysing a new dataset and the techniques used for data analysis. The results of some prominent studies in this field are discussed, for example, Lasisi et al.⁹ examined the CPU and volatility of the USA stock market by applying the GARCH-MIDAS framework for the period January 2000 to December 2021. They found that the critical information of the CPU can be exploited to improve the forecast of stock market volatility both in-sample and out-of-sample. Additionally, the uncertainty associated with climate change can benefit the portfolio returns as compared with those who do not recognise it. Li et al.⁶ investigated the impact of climate change on the Nasdaq, a USA stock market, and found that some factors of climate change have a significant positive impact on stock returns, whereas some other factors have a negative impact. Xu et al.¹⁰ assessed the impact of CPU on the return and volatility of the USA and Chinese stock markets by applying the GARCH (1,1) distribution lag nonlinear model known as DLNM and copula function for the period January 2000 to March 2022. The authors observed in the Chinese market that high CPU tends to decrease stock market return and increase volatility, but in the US market, in the short run, CPU decreases stock market return but increases it in the long term. Athari et al.¹¹ used the WC techniques to analyse the co-movement between CPU and the share prices of clean technology and renewable energy in the Canadian economy using monthly data gathered from 2013 to 2022. The CPU was greatly impacted by the Renewable Energy and Clean Technology Index (RECT) from 2014 to 2018, and after 2019, the CPU was the short- and medium-term cause of RECT. Furthermore, Chen et al.¹² investigated the impact of CPU on Chinese stock market volatility and concluded that CPU has a significant effect on stock price volatility. Lv and Li¹³ examined the impact of the CPU index on the volatility of the Chinese stock market, constituting different sectors, and found that CPU has a significant predictive power for the volatility of the materials, energy, consumer discretionary, industrials, utility and health care sectors. Ji et al.¹⁴ discussed the impact of global CPU and selected financial markets, namely the USA, UK, Canada, France and Japan. China and Germany and significant spillovers from global CPU to the financial markets were observed; spillovers from global CPU to the financial markets are mostly concentrated in the short term. Tedeschi et al.¹⁵ explored the impact of CPU on European financial markets; results revealed that CPU shocks have a significant effect on the financial indexes. Most existing research has concentrated on the USA, Chinese and Canadian economies, with less literature available on emerging economies such as India. This study fills this gap as it includes Indian benchmark indices Sensex and Nifty 50, presenting evidence on the impact of CPU on financial markets of developed and emerging economies. Dynamic spillover effects between the CPU and other stock indices have rarely been explored. This research addresses this gap by examining the direction and magnitude of spillover effects, offering new evidence on how policy shifts in climate policy in one market spill over into others, particularly worldwide. The research contributes to current literature by presenting policymakers and investors with real-world advice on how to mitigate risk through diversification of portfolios and building market-sensitive climate policy.

Objectives of the Study

To examine the short and long-term dynamic co-movements between CPU and prominent stock indices, including Nifty 50, Sensex, Nasdaq and Dow Jones.

To assess the WC relationship between CPU and stock market indices, with emphasis on the impact of CPU on the volatility of the market at different frequencies.

To shed light on investors’ diversification strategies for portfolios, with particular focus on asset choice under the scope of volatility caused by climate policy.

To suggest recommendations to policymakers on mitigating the financial market impact of climate policies.

Significance of the Study

The significance of the study is to identify the effect of CPU on the financial markets, particularly stock indices in India and the USA. The research describes how investors can adapt to market volatility caused by CPU more effectively. Investors can make effective portfolio diversification and risk management decisions by analysing the correlation and interdependence between CPU and stock indices. The study helps governments and regulators to develop policies that help to stabilise markets and reduce the adverse impacts of uncertainty. Understanding the impact of climate policy changes on stock indexes such as the Nasdaq and Dow Jones places into perspective the need to diversify into other investments. By focusing on less climate-exposed assets such as green bonds or renewable energy investments, investors are able to hedge CPU based risks. This study provides valuable leads to policymakers and financial institutions to include environmental risk analysis in the entire economic analysis. It emphasises the inclusion of climatic factors in investment decisions and market behaviour prediction. The study is part of the overall debate regarding how to make the financial system stable over the long term against heightened environmental risk.

Methodology

Wavelet Coherence

WC is a normalised measure of the relationship between two time series in both time and frequency domains. Following Torrence and Webster (1998)16, we define the wavelet coherence of two time series as

R_{x y}^{2} (s, τ) = \frac{{|S (s^{- 1} W_{x y} (s, τ))|}^{2}}{S (s^{- 1} {|W_{x} (s, τ)|}^{2}) \cdot S (s^{- 1} {|W_{y} (s, τ)|}^{2})},

where:

S(⋅) is known as a smoothing operator from both time and scale,

$R_{x y}^{2} (s, τ)$ is the squared WC, taking values between 0 (no coherence) and 1 (perfect coherence).

The smoothing operator ensures that the coherence is a localised measure that considers both time and frequency. A value close to 1 indicates strong coherence at that particular time and frequency.

Phase Difference

x(t) and y(t) are known as the phase difference between the two time series, which is given by the argument of the cross wavelet transform

ϕ_{x y} (s, τ) = \arg (W_{x y} (s, τ)) .

The phase difference helps in interpreting the time lag between the two time series. If $ϕ_{x ν} (s, τ) = 0$ , the series move in phase, while $ϕ_{x y} (s, τ) = π$ reveals that they navigate in opposite directions.

Interpretation of WC Plots

The WC plot displays the squared coherence $R_{x y}^{2} (s, τ)$ across time (horizontal axis) and frequency or scale (vertical axis). Regions with high coherence (close to 1) are highlighted, and the phase arrows indicate the lead–lag relationship:

Right arrows represent: In-phase movement.

Left arrows represent: Anti-phase (opposite) movement.

Arrows pointing upward: x(t) and y(t).

Arrows pointing downward: y(t) leads x(t).

Empirical Results

Table 1 exhibits the descriptive statistics. Sensex were highly volatile, followed by Dow Jones, Nifty 50, Nasdaq and CPU. Positive skewness was found among all the time series data of CPU, Nifty 50, Sensex, Nasdaq and Dow Jones. The result of the Jarque–Bera test indicates that the series does not follow the normal distribution. The kurtosis value of all the markets is greater than 3.00, which indicates that the majority of markets display leptokurtic distributions with maximum values concentrated around the means and thicker tails.

Table 1.

Descriptive Statistics.

	CPU	Nifty 50	Sensex	Nasdaq	Dow Jones
Mean	151.0457	10,403.57	34,662.78	7,217.154	22,258.54
Median	133.2113	9,026.675	29,544.58	5,979.675	20,876.38
Maximum	411.2888	2,236.90	73,651.35	16,379.46	39,807.37
Minimum	38.0929	4,624.300	15,454.92	2,109.420	9,774.020
Std. Dev.	70.1014	4,665.098	15,731.01	4,106.475	8,225.758
Skewness	0.801034	0.795147	0.795214	0.652357	0.323069
Kurtosis	3.339673	2.553630	2.490280	2.13340	1.842571
Jarque–Bera	18.77398	19.09798	19.52493	12.29996	17.7244
p value	.000084*	.00007*	.00005*	.002134*	.000187*
Total observations	168	168	168	168	168

Note: * indicates significance at 5%.

The strength of the relationships among the variables may be measured using correlation analysis. Table 2 represents the unconditional pairwise correlation between CPU, Nifty 50, Sensex, Nasdaq and Dow Jones. CPU was highly correlated with Nasdaq, and a negative correlation was observed with Dow Jones. USA investors can use these findings to diversify their portfolio into Dow Jones when there is a possibility of a change in climate policy. The Indian benchmark index Nifty 50 is highly correlated with the Nasdaq and negatively correlated with the Dow Jones. Hence, Indian investors should invest in Dow Jones to get the benefit of portfolio diversification. The Nifty 50 and Sensex were both positively correlated with CPU. Figures 1 –5 represent the movement of the CPU, Nifty 50, Sensex, Nasdaq and Dow Jones.

Table 2.

Unconditional Correlation Between CPU, Nifty 50, Sensex, Nasdaq and Dow Jones.

	CPU	Nifty 50	Sensex	Nasdaq	Dow Jones
CPU	1.0000	0.658613	0.663280	0.686669	−0.652944
Nifty 50	0.658613	1.0000	0.999508	0.954566	−0.897206
Sensex	0.663280	0.999508	1.0000	0.957005	−0.895925
Nasdaq	0.686669	0.954566	0.957005	1.0000	−0.905334
Dow Jones	−0.652944	−0.897206	−0.895925	−0.905334	1.0000

Figure 1.

Source: Figure created from compiled data using EViews software.

Figure 2.

Source: Figure created from compiled data using EViews software.

Figure 3.

Source: Figure created from compiled data using EViews software.

Figure 4.

Source: Figure created from compiled data using EViews software.

Figure 5.

Source: Figure created from compiled data using EViews software.

Network Connectedness

Diebold and Yilmaz’s (2009) connectedness analysis is a widely used framework in finance and economics to study the interconnectedness and spillovers among various assets, markets or variables. The result of dynamic connectedness is shown in Table 3. The table’s column shows the amount of spillover it receives from other assets or markets.

Table 3.

Network Connectedness.

Variables	CPU	Nifty 50	Sensex	Nasdaq	Dow Jones	From
CPU	89.09	3.51	3.30	3.26	0.84	10.91
Nifty 50	0.60	43.59	43.24	11.33	1.23	56.41
Sensex	0.62	43.04	43.38	11.57	1.39	56.62
Nasdaq	1.03	15.62	16.05	66.00	1.30	34.00
Dow Jones	2.72	1.92	2.05	6.92	86.79	13.21
To	4.97	64.09	64.65	32.68	4.77	171.15
Net	−5.94	7.68	8.02	−1.32	−8.44	42.79

Source: Author’s own analysis using R software.

Figure 6 shows directional linkages (arrows) between the CPU, Nifty 50, Sensex, Nasdaq and Dow Jones indices. The thickness of the arrows likely represents the strength of the connectedness, with thicker lines indicating stronger relationships. CPU is positioned with multiple connections, indicating that CPU has small spillover effects with other indices, particularly with Dow Jones, Nifty 50 and Nasdaq. The arrows point from one node to another, representing causation or influence direction. For instance, an arrow from Nifty 50 to Nasdaq and Sensex to Nasdaq suggests that the Indian market has a strong impact on Nasdaq. The arrow between CPU and Dow Jones is suggesting that CPU has some influence on Dow Jones. Figure 7 represents the spillover from CPU, Nifty 50, Sensex, Nasdaq and Dow Jones to the system, and Figure 8 represents the spillover from the system to CPU, Nifty 50, Sensex, Nasdaq and Dow Jones.

Figure 6.

Source: Figure created from compiled data using R software.

Figure 7.

Source: Figure created from compiled data using R software.

Figure 8.

Source: Figure Created from compiled data using R software.

WC Analysis

WC analysis is applied when analysing the relationship between two time series that may change over different time scales or frequencies. WC allows us to see how this relationship varies over time and across short- and long-term time horizons. To capture the dynamic linkages between CPU and Nifty 50, Sensex, Dow Jones and Nasdaq, WC approaches are applied, and it is presented in Figures 9 –12. The x-axis represents time (in years) and the y-axis represents the scale. Lower values (closer to 4) indicate higher frequencies or short-term relationships, while higher values (closer to 32) represent the long-term relationships. Blue areas represent low coherence, indicating weak or no relationship between Nifty 50 and CPU at those times and scales. Yellow, orange and red areas represent higher coherence. Rightward arrows mean that the series are ‘in-phase’ or move in the same direction. If the CPU increases, the Nifty 50 tends to increase as well, and vice versa. Leftward arrows mean that the series are ‘out of phase’ or move in opposite directions.

WC: CPU and Nifty 50

Yellow and orange areas show times and scales where the Nifty 50 and CPU have a stronger relationship. When the arrows point left, there is a strong negative correlation between the variables; when they point right, there is a strong positive correlation. Furthermore, the arrows pointing down, left-up and right-down suggest that the Nifty 50 variable is caused by the CPU variable. Alternatively, the CPU variable is caused by the Nifty 50 factor, as indicated by the arrows pointing up, left-down and right-up. If the CPU increases, the Nifty 50 tends to decrease, and vice versa. Upward arrows indicate that the CPU ‘leads’ Nifty 50. In other words, changes in CPU tend to change in the Nifty 50, suggesting that CPU may be a predictor of market movements (Figure 9).

Figure 9.

Source: Figure created from compiled data using R software.

WC: CPU and Sensex

High-coherence region was observed around a specific year at four and eight frequency levels during 2013–2015, this suggests that CPU and Sensex were strongly related at over roughly 4- and 8-month horizons, meaning that CPU affects Sensex in the short run. If the CPU leads Sensex (upward arrows), this might imply that rising uncertainty around climate policy precedes declines or increases in Sensex, as investors adjust their portfolios based on perceived risks from climate policies. If Sensex leads CPU (downward arrows), it may indicate that market trends anticipate changes in CPU, possibly due to investor sentiment or economic factors that preempt policy changes (Figure 10).

Figure 10.

Source: Figure created from compiled data using R software.

WC: CPU and Dow Jones

The WC plot reveals that the relationship between CPU and the Dow Jones varies over time, and a strong short-term relationship was observed during 2012–2013. Areas of high coherence indicate a significant interaction between CPU and the stock market, with specific patterns emerging at short- and long-term time horizons (Figure 11).

Figure 11.

Source: Figure created from compiled data using R software.

WC: CPU and Nasdaq

At four frequency levels in the year 2021–2023, CPU leading NASDAQ (upward arrows) suggests that increases in CPU could trigger market responses, where investors adjust portfolios based on anticipated industry shifts. NASDAQ leading CPU (downward arrows) may indicate that NASDAQ trends could predict shifts in policy direction, possibly due to rising market pressures for sustainable practices or climate-focused investments. For investors, understanding the periods and scales where CPU strongly influences NASDAQ can help in adjusting portfolios accordingly, especially in sectors such as technology, where climate policy uncertainties may have a greater impact. Clear policies can mitigate market volatility and build investor confidence in climate-related decisions (Figure 12).

Figure 12.

Source: Figure created from compiled data using R software.

Discussion

The results of the research contribute a few important additions to the dynamic interplay between CPU and major stock indexes, namely the Sensex, an Indian benchmark stock market index. Diebold and Yilmaz’s Connectedness and WC techniques were employed in order to reveal severe CPU-market volatility interactions with different time scales and horizons.

Sensex and CPU

The results suggest that the Sensex is highly sensitive to CPU, particularly in the short term, as is seen from the WC test. This means that the investor sentiment in the Indian market is sensitive to changes in global and domestic climate policies. For instance, episodes of high CPU, such as large cross-border deals (e.g., the Paris Agreement) or actions by the Indian government, are likely to cause higher volatility in the Sensex.

In the long run, however, the study finds that Sensex stabilises even in the presence of CPU-led volatilities. This corroborates the belief that long-run market fundamentals of developing countries such as India are not vulnerable to short-run policy uncertainties on account of the reality that companies come to fit into new regulatory regimes with the passage of time.

Conclusion

The main aim of the present study is to examine the WC and dynamic connectedness between CPU and Nifty 50, Sensex, Dow Jones and Nasdaq for the period 1 April 2010 to 31 March 2024. Diebold and Yilmaz’s (2009) connectedness and WC approach were applied for data analysis. Understanding the connectedness between the CPU and stock indices can guide investors in diversifying their portfolios. Since CPU impacts indices such as Dow Jones and Nasdaq more heavily, global investors might consider balancing their portfolios with assets that are less influenced by climate policy changes to mitigate potential risks. CPU was highly correlated with Nasdaq, and a negative correlation was observed with Dow Jones. US investors can use these findings to diversify their portfolio into Dow Jones when there is a possibility of a change in climate policy. The Indian benchmark index Nifty 50 is highly correlated with the Nasdaq and negatively correlated with the Dow Jones. This neuroeconomic perspective on market behaviour demonstrates how policy-induced uncertainty can cause investors to move away from an investment approach and risk aversion, guiding choices both on the basis of rational analysis and emotional response. The workings of such market forces are significant not only to the diversification of portfolios and risk management but also to policymakers who seek to enhance market stability. By including less computationally intensive assets such as green bonds or eco-investments, investors can decrease climate policy-induced volatility. They assist in providing a richer neuroeconomic framework linking environmental cues, policy cues and money behaviour in offering insights to the advantage of both neuroscientists and economic decision-makers.

Implications for Investors

Since CPU and Sensex volatility go hand in hand, investors can hedge against risk by diversification in the least likely foreign assets to change with climate policy variations. For instance, Sensex’s negative correlation with Dow Jones can be employed for cross-market hedging. Along with this, one can also look for such sectors that are less exposed to climate policy or are benefited directly by them, that is, renewable energy, technology and green infrastructure. These sectors have the ability to give consistent returns even in conditions of uncertainty surrounding climate policies and act as a hedge against broad market risk.

Policy Implications

Policymakers can observe how climate policies are affecting financial markets. If the CPU is causing significant fluctuations in key indices, it suggests that markets are sensitive to regulatory changes. This feedback can help in designing policies that minimise adverse market reactions while promoting sustainable development. Policymakers might also consider how they communicate upcoming climate regulations to the public. Clear, predictable policy announcements could mitigate market uncertainty and make transitions smoother for the financial markets.

The article determines that climate policy announcements have a considerable influence on market stability. The policymakers should therefore aim to make climate policies clear and foreseeable manner so that market shocks can be avoided. Policy uncertainty may lead to extreme market reactions, such as the short-run volatility of the Sensex during periods of higher CPU.

Furthermore, coordinated international climate policy is identified by the study as an important requirement. Since the Indian economy is interlinked with global indices such as the Nasdaq and Dow Jones, policy actions elsewhere can have spillover effects on Indian markets. Therefore, more international policy coordination in climate policy can reduce cross-border volatility in the markets, and this can make the global financial system more stable.

Authors’ Contribution

All authors contributed equally.

Statement of Ethics

Ethical permission was not required for this research work.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Data Availability

Available on request.

ORCID iDs

Pravin Kumar Agrawal

Shubham Goenka

Supriya Agrawal

References

Yang

, Huang

and Zhang

Decomposing climate risks in stock markets. International Monetary Fund [Internet]. 2023 [cited 2025 Jan 8]. Available from: https://www.imf.org/en/Publications/WP/Issues/2023/06/30/Decomposing-Climate-Risks-in-Stock-Markets-534307

Koh

, Zeng

, Sarira

, . Carbon prospecting in tropical forests for climate change mitigation. Nat Commun 2021; 12(1): 1271. DOI: 10.1038/s41467-021-21560-2

Boulange

, Hanasaki

, Yamazaki

, . Role of dams in reducing global flood exposure under climate change. Nat Commun 2021; 12(1): 417. DOI: 10.1038/s41467-020-20704-0.

Worsening impact of climate change. Indian Express [Internet]. 2024 Nov 1 [cited 2025 Jan 8]. Available from: https://indianexpress.com/article/explained/explained-climate/worsening-impact-climate-change-9650270/

Battiston

, Mandel

, Monasterolo

, . A climate stress-test of the financial system. Nat Clim Chang 2017; 7(4): 283–288.

, Liu

and Pan

A study of impact of climate change on the US stock market as exemplified by the NASDAQ 100 index constituents. Sci Rep 2024; 14(1): 15468. DOI: 10.1038/s41598-024-66109-7

10 key moments in the climate change fight. NDTV [Internet]. 2024 Nov 1 [cited 2025 Jan 8]. Available from: https://www.ndtv.com/world-news/10-key-moments-in-the-climate-change-fight-6938847

Diebold

and Yilmaz

Measuring financial asset return and volatility spillovers, with application to global equity markets. Econ J 2009; 119(534): 158–171.

Lasisi

, Omoke

and Salisu

AA.

Climate policy uncertainty and stock market volatility. Asian Econ Lett 2022; 5(2). DOI: 10.46557/001c.37246

10.

, Huang

, Lucey

, . The impacts of climate policy uncertainty on return, volatility, correlation and tail dependence of China’s and US stock markets. SSRN 2022. DOI: 10.2139/ssrn.4101828

11.

Athari

and Kirikkaleli

How do climate policy uncertainty and renewable energy and clean technology stock prices co-move? Evidence from Canada. Empir Econ 2025; 68(1): 353–371. DOI: 10.1007/s00181-024-02643-7

12.

Chen

, Zhang

and Weng

Does climate policy uncertainty affect Chinese stock market volatility?

Int Rev Econ Finance 2023; 84: 369–381. DOI: 10.1016/j.iref.2022.11.030

13.

and Li

Climate policy uncertainty and stock market volatility: Evidence from different sectors. Finance Res Lett 2023; 51: 103506. DOI: 10.1016/j.frl.2022.103506

14.

, Ma

, Zhai

, . Global climate policy uncertainty and financial markets. J Int Financ Mark Inst Money 2024; 95: 102047. DOI: 10.1016/j.intfin.2024.102047

15.

Tedeschi

, Foglia

, Bouri

, . How does climate policy uncertainty affect financial markets? Evidence from Europe. Econ Lett 2024; 234: 111443. DOI: 10.1016/j.econlet.2023.111443

16.

Torrence

and Webster

PJ.

Interdecadal changes in the ENSO–monsoon system. J Climate 1999;12(8):2679–2690.