Research Articles (Economics)

Permanent URI for this collectionhttp://hdl.handle.net/2263/2391

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Housing market variables and predictability of state-level stock market volatility of the United States : fundamentals versus sentiments in a mixed-frequency framework
Salisu, Afees A.; Gupta, Rangan; Cepni, Oguzhan (Elsevier, 2026-01)
This paper utilizes the generalized autoregressive conditional heteroscedasticity–mixed data sampling (GARCH‑MIDAS) approach to predict the daily volatility of state‑level stock returns in the United States (US) from monthly state and national housing price returns. We find that housing price returns generally have a negative effect on state‑level volatility. More importantly, the GARCH‑MIDAS model augmented with these predictors significantly outperforms the benchmark GARCH‑MIDAS model with realized volatility (GARCH‑MIDAS‑RV) over short‑, medium‑, and long‑term forecasting horizons for 90 % of the states; the performance of state and national housing returns is virtually indistinguishable. These superior forecasting results persist when housing price returns are replaced with housing permits and housing‑market media‑attention indexes, suggesting an overwhelming role for housing‑market variables—both traditional and behavioral—in forecasting state‑level stock‑return volatility. Our findings have important implications for investors and policymakers. HIGHLIGHTS • Housing price returns predict state-level stock volatility in the US. • GARCH-MIDAS-HPR outperforms benchmark models across most states. • Predictive gains hold for short, medium, and long forecasting horizons. • Housing permits and media indexes also forecast volatility effectively. • Findings inform investor strategies and guide state-level policy action.
Housing search activity and quantiles-based predictability of housing price movements in the USA
Gupta, Rangan; Moodley, Damien (Emerald, 2026-12)
PURPOSE : Recent evidence from a linear econometric framework infers that housing search activity, captured from Google Trends data, can predict housing returns for the USA at a national and regional (metropolitan statistical area [MSA]) level. Based on search theory, the authors, however, postulate that search activity can also predict housing returns volatility. This study aims to explore the possibility of using online search activity to predict both housing returns and volatility. DESIGN/METHODOLOGY/APPROACH : Using a k-th order non-parametric causality-in-quantiles test allows us to test for predictability in a robust manner over the entire conditional distribution of both housing price returns and its volatility (i.e. squared returns) by controlling for nonlinearity and structural breaks that exist in the data. FINDINGS : The analysis over the monthly period of 2004:01 to 2021:01 produces results indicating that while housing search activity continues to predict aggregate US house price returns, barring the extreme ends of the conditional distribution, volatility is relatively strongly predicted over the entire quantile range considered. The results carry over to an alternative (the generalized autoregressive conditional heteroskedasticity-based) metric of volatility, higher (weekly)-frequency data (over January 2018–March 2021) and to over 84% of the 77 MSAs considered. ORIGINALITY/VALUE : To the best of the authors’ knowledge, this is the first study regarding predictability of overall and regional US housing price returns and volatility using search activity, based on a non-parametric higher-order causality-in-quantiles framework, which is insightful to investors, policymakers and academics.
Energy poverty and private sector in sub-Saharan Africa : role of governance effectiveness
Pondie, Thierry Messie; Kwakwa, Paul Adjei (Elsevier, 2026-05)
This study examines the effect of energy poverty on private sector development in sub-Saharan Africa. Unlike previous studies that mainly focused on macroeconomic factors such as GDP, financial development, and inflation, it places energy poverty at the center of the analysis. The study is conducted in the specific context of sub-Saharan Africa, where limited access to modern energy remains a major structural constraint. It also introduces governance as a key moderating factor. In particular, it incorporates the rule of law and control of corruption to assess how institutional quality can reduce the negative effects of energy poverty. The analysis uses panel data from 45 sub-Saharan countries covering the period 2000–2022. To ensure robust results, the study applies advanced econometric methods, including Lewbel Two-Stage Least Squares, Kinky Least Squares, Generalized Method of Moments, and quantile regressions. The findings show that energy poverty significantly hinders private sector growth. The quantile results reveal that this effect varies across different levels of development. Strong governance significantly weakens the negative impact of energy poverty, especially in countries with lower private sector performance. Overall, the results provide new and robust empirical evidence on the joint role of energy access and governance. They suggest that sustainable private sector development requires both improved energy infrastructure and stronger institutions. These findings offer clear guidance for policymakers seeking inclusive and durable growth. HIGHLIGHTS • Energy poverty severely constrains the private sector in Sub-Saharan Africa. • Good governance reduces this negative impact. • High-performing firms are the most affected. • Sustainable growth requires reliable energy and strong institutions.
Prevalence of plastic waste as a household fuel in low-income communities of the Global South
Bharadwaj, Bishal; Gates, Tara; Rose, Sobia; Antriyandarti, Ernoiz; Praveena, Sarva Mangala; Oranu, Chizoba Obianuju; Borthakur, Monjit; Dhungana, Pramesh Kumar; Shazly, Aminath; De-la-Torre, Gabriel Enrique; Allison, Ayse Lisa; Abeywardhana, Dinushika Madhushani Yapa; Mabaso, Sizwe; Adom, Philip Kofi; Banga, Margaret; Dlamini, Witness; Kabera, Telesphore; Bohlmann, Jessika Andreina; Areeprasert, Chinnathan; Veettil, Bijeesh Kozhikkodan; Dieudonne Shukuru, Wasso; Nkhwanana, Nyaladzani; Kammwamba, Alice; Rai, Rajesh Kumar; Conteh, Bakary; Erasmus, Victoria Ndinelago; Agbere, Sadikou; Phonhalath, Keophousone; Njoroge, Hope; Glenn, Darcy; Ishuga, Esther; Mugisho, Gilbert Mubalama; Moolla, Raeesa; Hounnou, Femi E.; Guloba, Madina Mwagale; Damiran, Ulemj; Vuthaluru, Hari; Mulagetta, Yacob; Jeuland, Marc; Gates, Ian D.; Ashworth, Peta (Nature Research, 2026-01)
Anecdotal evidence suggests that households burn plastics to manage waste and help satisfy their energy demand. To examine the prevalence, extent, and reasons for using plastic waste as household fuel, we report on a survey with 1018 key informants from cities in 26 countries in the Global South. Informants were purposively selected due to their familiarity with the living conditions in their communities. One-third of respondents reported being aware of plastic waste burning, with some reporting that their households engaged in this practice. Analyses of the data reveal significant correlations of plastic waste burning with both supply factors, such as, the massive amount of waste generated (p = 0.000), expensive clean fuels (p = 0.004), and demand factors, including self-management of waste (p = 0.000). Expanding essential public waste management services and implementing programs that enhance the affordability of clean energy technologies, especially among marginalized and low-income communities, could reduce this health- and environment-damaging practice.
Climate risks and predictability of the conditional distributions of rare earth stock returns and volatility
Polat, Onur; Gupta, Rangan; Bouri, Elie; Brahim, Mariem (Springer, 2026-02)
Using a nonparametric causality-in-quantiles test, we examine the predictability of rare earth stock returns and volatility based on physical and transition climate risks over the period 2nd January 2008 to 31st January 2025. Our results indicate that, although the linear Granger causality test fails to show any evidence of predictability due to model misspecifications arising from nonlinearity and structural breaks, the nonparametric causality-in-quantiles test shows significant predictability over the entire conditional distribution of rare earth stock returns and volatility. The evidence of predictability is robust to alternative choices of rare earth stock indexes, measures of climate risk, conditional estimates of volatility, and multiple macroeconomic and financial control variables. Further analyses involving the signs of the causal impact and a rolling-window estimation reveal that returns are negatively impacted over the range of lower conditional quantiles till the median, corresponding to weak global conditions; volatility, however, is increased over its entire conditional distribution. The implications of our findings are discussed.
Can monetary and fiscal policy account for South Africa's stagnation?
Loate, Tumisang Bertha; Viegi, Nicola (Routledge, 2026)
This paper examines the interaction between macroeconomic variables and the fiscal and monetary policy mix between 2012 and 2019, a period characterized by increased public debt and risk premium and low economic growth. We use a large Bayesian vector autoregressive model and find that monetary and fiscal policy fails to account for the observed lower real gross domestic product between 2012 and 2019. Based on their historical relationship, the results indicate that we should have observed much higher growth, especially during the 2015 to 2019 period. In addition, we find little evidence that the low growth during the period can be rationalized by the much-criticized anti-growth monetary policy.
Political geography and stock market volatility : the role of political alignment across sentiment regimes
Cepni, Oguzhan; Demirer, Riza; Gupta, Rangan; Pierdzioch, Christian (Wiley, 2026-02)
We study the nexus between political geography and stock market volatility by examining the interrelation between political geography and the predictive relation between the state- and aggregate-level stock market volatility via recently constructed measures of political alignment. Using data for 1994–2023 and random forests, we show that the importance of the state-level volatilities as drivers of aggregate volatility displays considerable variation in the cross-section and across time. Stronger political alignment of a state with the ruling party is associated with a lower contribution of the state's volatility to aggregate volatility. This negative link is significant during high-sentiment periods.
Time-varying spillover of multi-scale positive and negative bubbles in stock and oil markets
Foglia, Matteo; Gupta, Rangan; Caraiani, Petre; Pacelli, Vincenzo (Elsevier, 2026-01)
The objective of this paper is to analyze time-varying spillover between bubbles in oil and stock markets of the U.S. In this regard, we first use the Multi-Scale Log-Periodic Power Law Singularity Confidence Indicator (MS-LPPLS-CI) approach to detect both positive and negative bubbles in the short-, medium and long-term in the two markets. In the second-step, we utilize a Time-Varying Parameter Vector Autoregressive (TVP-VAR) model to conduct the spillover analysis among the indexes of oil and stock positive and negative bubbles. Based on data covering the monthly period of January 1999 to June 2025, we find that negative bubble spillovers are significantly stronger and more directional than positive ones, with the U.S. equity market emerging as the transmitter to the oil market post-2008. This represents a structural shift from the traditional oil-to-equity transmission paradigm. Moreover, spillover effects are most pronounced at short- and medium-term horizons, intensifying during crisis periods. Our findings suggest that oil is increasingly behaving as a financial asset rather than a physical commodity, with important implications for portfolio diversification and risk management. HIGHLIGHTS • TVP-VAR spillover between oil and stock bubbles analyzed. • MS-LPPLS-CIs detect positive and negative bubbles in the short-, medium and long-term. • Negative bubble spillovers are significantly stronger and more directional. • U.S. equity market is the transmitter to the oil market post-2008. • Spillover effects most pronounced at short- and medium-term horizons.
The effects of uncertainty on economic conditions across US states : the role of climate risks
Sheng, Xin; Gupta, Rangan; Liao, Wenting; Cepni, Oguzhan (Wiley, 2026-02)
We analyze the impact of uncertainty on the Economic Conditions Index (ECI) of the 50 US states in a panel data set-up, over the weekly period of the 3rd week of April 1987 to the 4th week of March 2023. Using impulse response functions (IRFs) from a linear local projections (LP) model, we show that uncertainty, as captured by the stochastic volatility (SV) of the ECIs, negatively impacts ECI in a statistically significant manner. More importantly, using a nonlinear LP model, the IRFs reveal that the adverse effect of uncertainty is significantly stronger under the high-regime of climate risks when compared to the low-regime of the same. Understandably, our results have important policy implications.
Supply bottlenecks and machine learning forecasting of international stock market volatility
Somani, Dhanashree; Gupta, Rangan; Karmakar, Sayar; Plakandaras, Vasilios (Elsevier, 2025-12)
This study explores the information value of the daily Supply Bottlenecks Index (SBI) – derived from newspaper articles – to forecast daily return volatilities of seven major developed stock markets: China, France, Germany, Italy, Spain, the UK, and the US. Volatility is measured using the interquantile range, obtained through an asymmetric slope autoregressive quantile regression model applied to stock returns to estimate conditional quantiles. From this, we derive key distributional moments including skewness, kurtosis, and lower- and upper-tail risks, which are then incorporated into a linear forecasting framework alongside leverage effects. Using Lasso shrinkage techniques to address potential overfitting, we find that the model incorporating higher-order moments outperforms a benchmark model based solely on own- and cross-country volatilities. Notably, the predictive accuracy improves further when supply constraint indicators from all seven countries are included. These results hold important implications for investors as we later highlighted.
Investment adjustment costs and growth dynamics
Gupta, Rangan; Ma, Wei (Elsevier, 2025-12)
We develop a monetary endogenous growth overlapping generations model characterized by investment adjustment costs as a negative function of productive government expenditures, and an inflation-targeting central bank. We show that growth dynamics arise, otherwise not possible in a standard monetary endogenous growth model with a money growth-rule and an exogenous adjustment cost parameter. Furthermore, hinging crucially on the strength of the response of the adjustment cost to productive public spending, single or multiple equilibria emerge, with the high-growth (low-growth) equilibrium in the latter case being stable (unstable), but locally indeterminate (locally determinate).
Macroeconomic shocks and SMME’s employment in South Africa : evidence from ARDL and ECM approaches
Adesile, Olusegun; Habanabakize, Thomas (Adonis and Abbey Publishers, 2025-12)
Small and medium enterprises (SMMEs) play a significant role in any country’s economy and specifically in job creation. However, the potency of SMMEs depends on various factors that include macroeconomic factors such as fuel or petroleum price, interest rate, and exchange rate fluctuations. The current study aims to investigate macroeconomic shocks and SMME’s employment in South Africa. To achieve this objective, the Autoregressive Distributed Lag (ARDL) and Error-Correction Models were applied to time series data spanning from 2009 to 2022. The study findings revealed that petrol price and interest rate negatively influence SMME’s employment in both the long-run and the short-run. However, the exchange rate was found to have a positive effect on employment in the long-run as well as the short-run. Consequently, the study for the South African SMME employment growth The study recommends that the South African government adopt “Sustainable Energy and Economic Growth Policy” as this policy could assist in stabilising energy costs, reducing fuel price volatility through strategic reserves or subsidies, and implementing inflation-control measures such as improving supply chains and promoting local production. By addressing high energy costs and inflation, the policy would create a favourable environment for SMMEs to thrive, invest, expand, and generate employment, leading to overall economic growth
Climate policy uncertainty and the forecastability of inflation
Salisu, Afees A.; Ogbonna, Ahamuefula E.; Gupta, Rangan; Zhang, Yunhan (Elsevier, 2026-03)
We investigate the predictive content of climate policy uncertainty (CPU) for forecasting the inflation rate of the United States (US) over the monthly period of 1987:05–2024:11. We evaluate the performance of our proposed CPU-based predictive model, estimated via the Feasible Quasi Generalized Least Squares (FQGLS) approach, against a historical average benchmark model, with the FQGLS technique adopted to account for heteroscedasticity and autocorrelation in the data. We find statistical evidence in favour of a CPU-based model relative to the benchmark, as well as in the case of an extended model involving physical risks of climate change and financial and macroeconomic factors, extracted from a large data set, when CPU is included. The predictive superiority of climate policy-related uncertainties relative to the historical mean remains robust across alternative local and global CPU metrics, as well as in a mixed-frequency setup, given the availability of high-frequency (weekly) CPU data. Moreover, the importance of local- and global-CPUs is also found to hold for forecasting the inflation rates of 11 other advanced and emerging countries, in a statistically significant manner relative to the historical average model. Across all 12 economies, own- and global-CPUs perform equally well in forecasting the respective inflation rates. The general importance of uncertainties surrounding policy decisions to tackle climate change in shaping the future path of inflation, understandably, carries implications for the monetary authority.
The US-China tension, global supply disruptions and the agricultural commodity markets : a dynamic multivariate panel data analysis
Salisu, Afees A.; Abdulhakeem, Abdulhameed (Emerald, 2026)
PURPOSE : The US–China trade friction represents a major geopolitical shock that disrupts global trade flows, supply chains, and commodity markets. This study aims to provide new evidence on how US–China trade tensions (UCT) influence the realized volatility of agricultural commodity prices, with a focus on both futures and spot markets, and to examine the differential responses of these markets to geopolitical and supply chain shocks. DESIGN/METHODOLOGY/APPROACH : Using a dynamic multivariate analysis with data spanning from April 1998 to February 2024, which encompasses multiple trade cycles, including the 2018–2019 US–China trade war and the post-pandemic recovery, we uncover how geopolitical tensions transmit to commodity markets via the global disruption channel. FINDINGS : Our findings show that the futures market tends to exhibit stronger and more persistent volatility in response to trade tensions than spot markets, reflecting the forward-looking nature of futures trading and the role of speculation in amplifying uncertainty. Moreover, our robustness analysis confirms that the volatility response is more pronounced for certain commodities directly exposed to the US-China trade nexus. In contrast, globally traded soft commodities exhibit more muted reactions. ORIGINALITY/VALUE : This study makes three key contributions. We (1) introduced the US-China trade tensions (UCT) as a novel source of geopolitical uncertainty in global agricultural commodity markets (2) employed the new indices of the US-China Tension Index and the Global Supply Chain Pressure Index (GSCPI) to capture multidimensional global risks (3) applied the dynamic multivariate panel framework to assess how UCT shocks propagate through commodity spot and futures markets, influencing volatility and price dynamics.
Climate risks and green stocks in Vietnam
Salisu, Afees A. (Emerald, 2026)
PURPOSE : Vietnam is among the countries most vulnerable to tropical cyclone risks, and its carbon-intensive production model influences its climate change trajectory. Nevertheless, various initiatives have been undertaken to tap into the country’s green economy potential, and I advance the related literature by exploring the connection between climate risks and green assets in Vietnam. DESIGN/METHODOLOGY/APPROACH : Firstly, by employing the predictive models, I examine the predictive power of climate risks for the returns of green assets in Vietnam between 2010 and 2023 using monthly data. Secondly, to address endogeneity and heteroscedasticity, I employ the feasible quasi-generalized least squares estimator, evaluating both in-sample and out-of-sample connections between climate risks and green assets in Vietnam. FINDINGS : My findings include the following: (1) Green stocks in Vietnam do effectively hedge against climate risk in recent samples that coincide with commitments to international climate agreements, suggesting the importance of data frames and the government’s commitment to modelling climatic outcomes. (2) Classification of assets based on the Vietnam Sustainability Index (VNSI) provides more theoretically compelling results, highlighting the need for robust measures of sustainability. (3) Controlling for key fundamentals, such as oil prices and exchange rate fluctuations, is essential to avoid model misspecification and potential overestimation of climate risk effects on green investment returns. (4) My findings show that incorporating climate risk into the predictive model for green asset returns significantly enhances forecast accuracy of the asset returns compared to benchmark models, such as the historical average and random walk, which overlook this risk factor. ORIGINALITY/VALUE : I provide two major contributions to the literature. (1) I investigate the predictive power of climate risks for the returns of green assets in Vietnam. (2) I conduct both the in-sample and out-of-sample predictability of the connections, as significant in-sample predictability outcomes do not necessarily translate into improved out-of-sample forecasts.
Forecasting growth-at-risk of the United States : housing price versus housing sentiment or attention
Cepni, Oguzhan; Gupta, Rangan; Pierdzioch, Christian (Springer, 2025-09-09)
We examine the predictive power of national housing market-related behavioral variables, along with their connectedness at the state level, in forecasting US aggregate economic activity (such as the Chicago Fed National Activity Index (CFNAI) and real Gross Domestic Product (GDP) growth), as opposed to solely relying on state-level housing price return connectedness. Our results reveal that while standard linear regression models show statistically insignificant differences in forecast accuracy between the connectedness of housing price returns and behavioral variables, quantile regression models, which capture growth-at-risk, demonstrate significant forecasting improvements. Specifically, state-level connectedness of housing sentiment enhances forecast accuracy of the CFNAI at lower quantiles of economic activity, indicative of downturns, whereas connectedness of housing attention is more effective at upper quantiles, corresponding to upturns. The results for GDP growth suggest that, while both sentiment and attention contribute to forecasting performance at lower quantiles, only attention improves forecasting performance at upper quantiles. In terms of statistical significance, the results for GDP growth, however, are less conclusive than those for the CFNAI. Taken together, these findings underscore the importance of incorporating regional heterogeneity and behavioral aspects in economic forecasting.
Energy market uncertainty and economic conditions at the global and US State levels
Salisu, Afees A.; Olaniran, Abeeb Olatunde (Springer, 2026-01)
This study evaluates the predictability of energy uncertainty in relation to economic activity across the global and the large open economy of the United States. Two distinct objectives guide the research: first, to explore the nexus between energy uncertainty and economic activity using various metrics, and second, to examine how well energy uncertainty enhances the forecast performance of economic activity across three different benchmark models, including a random walk with and without drift, and a historical average. The analysis incorporates two lag structures to capture additional dynamics, ensuring a comprehensive understanding of the relationship between energy uncertainty and economic activity. Results indicate that heightened energy uncertainty generally stifles economic activity, although this effect weakens over a longer lag structure. This finding is consistent for both in-sample and out-of-sample forecasts, and remains robust even when certain fundamentals are incorporated as controls, highlighting the strength of the research. These findings hold significant implications for both micro- and macroeconomic perspectives, underscoring the potential contribution of this research to the field of economics. The implications for policymakers are particularly noteworthy, as they provide valuable insights for decision-making in the energy sector.
The employment-effects of greening the South African economy
Njokwe, Getrude; Bohlmann, Jessika Andreina; Chitiga-Mabugu, Margaret; Omotoso, Kehinde Oluwaseun; Mushongera, Darlington (Taylor and Francis, 2025-07-03)
This study aims to develop a method for classifying occupations into green and non-green jobs and examines the impact of the green economy on employment. It focuses on patterns across industries and the characteristics of individuals employed as the country transitions to a green economy. The study utilises the local Organising Framework for Occupations (OFO) and the International Occupational Information Network (O*NET) to categorise jobs, applying parametric and non-parametric approaches to identify the determinants of green jobs. The proportion of green jobs in South Africa has been slowly increasing, constituting 13.8% of all jobs in 2024. These jobs are mainly found in utilities, mining, construction, and finance. They are primarily occupied by younger individuals with moderate education. Most positions are held by men, with white and black individuals as the main demographic groups, largely within the formal sector. These findings are important for policies promoting inclusive green economy growth.
Do investors in clean energy ETFs herd? The role of climate risks
Babalos, Vasilios; Sibande, Xolani; Bouri, Elie I.; Gupta, Rangan (Emerald, 2026-01)
PURPOSE : This study aims to investigate herding behaviour in US Clean Energy (CE) exchange-traded funds (ETFs) and examine the role of climate risks in influencing such behaviour over the period from May 1, 2016, to June 19, 2024. DESIGN/METHODOLOGY/APPROACH : We employ a baseline herding model and extend it to examine asymmetric effects across market conditions. The analysis incorporates time-varying herding measures and examines the impact of both transitional and physical climate risks on herding probability using regression techniques. FINDINGS : The baseline model reveals significant herding behaviour in CE ETFs. The extended model indicates that herding is present in both down and up markets, with a stronger effect in down markets, suggesting asymmetry. Herding is also found to be time-varying. Notably, high levels of transitional climate risk reduce the probability of herding in CE ETFs, whereas physical climate risk does not exert any significant impact on herding probability. RESEARCH LIMITATIONS/IMPLICATIONS : The study focuses specifically on US CE ETFs over a defined period, which may limit generalizability to other markets or asset classes. The findings provide insights into the behavioural dynamics of sustainable investment markets during periods of varying climate risk. PRACTICAL IMPLICATIONS : The results suggest that high levels of transitional climate risk encourage market efficiency in CE ETFs and promote climate hedging behaviour by investors. This has important implications for portfolio managers and policymakers in understanding market dynamics in sustainable finance. ORIGINALITY/VALUE : This study provides novel empirical evidence on the relationship between climate risks and herding behaviour in CE ETFs, contributing to the growing literature on behavioural finance in sustainable investment markets.
Forecasting the volatility of stock returns in the G7 countries over centuries : the role of climate risks
Bouri, Elie I.; Gupta, Rangan; Liphadzi, Asingamaanda; Pierdzioch, Christian (Springer, 2026-01)
We investigate whether changes in temperature anomalies, along with their second, third, and fourth statistical moments, can serve as indicators of physical climate risks and provide valuable insights for forecasting historical stock return volatility in Canada, France, Germany, Italy, Japan, the United Kingdom (UK), and the United States (US) the G7 countries. This analysis controls for factors such as leverage, skewness, and excess kurtosis in stock price fluctuations. Using extensive monthly data spanning from 1915 to 2024 for Canada and Italy, from 1898 to 2024 for France, from 1870 to 2024 for Germany, from 1914 to 2024 for Japan, from 1693 to 2024 for the UK, and from 1791 to 2024 for the US, our findings indicate that the moments of stock markets play a more significant role than climate risks in accurately forecasting stock return volatility. Further analysis confirms that the impacts of climate risks are already reflected in the statistical moments of stock returns. We discuss the implications of these findings for investment decisions and economic policy, suggesting that investors and policymakers in the G7 countries should focus more on statistical moments rather than physical climate risks when producing forecasts of stock market volatility for their decision-making processes.

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