Climate, energy, and geopolitical risks in African stock markets: a comparative TVP-VAR and QVAR approach

Purpose. The most recent conflicts have demonstrated that geopolitical risk has evolved into a significant issue that has an impact on the global food markets. Through the use of bi-wavelet coherence analysis, the study aimed to establish the ways in which geopolitical risk and climate policy uncertainties influences the food commodity market using Geopolitical Risk Index (GPR index), Climate Policy Uncertainty Index (CPU index) and the five components that make up the FAO Food Price Index (FPI). Methodology / approach. The study used monthly data spanning from January 1990 to March 2024. Geopolitical risk was measured using the GPR index developed through textual analysis of news articles. CPU index, developed using similar textual analysis, is used to represent the uncertainties related to climate change risk. The FAO’s FPI constituents were used to represent global food commodity market. The research applied advanced econometric methods including Johansen cointegration tests, Toda-Yamamoto causality analysis, Brock-Dechert-Scheinkman (BDS) nonlinearity tests, and bi-wavelet coherence analysis. Wavelet coherence analysis was particularly focused due to its capability to capture dynamic, time-frequency relationships among non-stationary data series. Results. The study found two significant long-run cointegrating relationships among GPR, CPU and FPI constituents. Causality tests indicated that geopolitical risk significantly influenced climate policy uncertainty but not vice versa. Wavelet analysis revealed that GPR and vegetable oil has more strong co-movement, and it is also the same in the case of CPU. CPU has a leading influence on GPR, which means that policy uncertainties lead to increased geopolitical tensions. Uncertainties in climate policies have an effect on food commodity market in the short run. Whereas, GPR affects cereals during geopolitical tension periods. In the case of dairy products, time varying co-movements in the short run could be witnessed whereas in the long run medium co-movement could be seen. Volatilities occur in the prices of vegetable oils during periods of crisis which can exacerbate prices of other food commodities, which can lead to food security issues. Originality / scientific novelty. The originality of the study lies in the fact that the main focus is on GPR, CPU and five constituents of FAO’s FPI. Moreover, the study uniquely incorporates CPU index as a proxy to climate change risk and its impact on food commodity market. Most of the studies focus on the spillover effect of geopolitical risk on different classes of asset. Significant number of literatures focus on the spillover effect on oil market, stock market and commodities market. However, there are only limited studies that focus on food commodity market. In addition, analysing these factors provides a deeper understanding of how they affect food security and market dynamics. This innovative approach offers valuable insights to policymakers, investors and stakeholders of food commodity market. Practical value / implications. Creating a more economically sustainable environment is the goal of every country, which requires joint efforts by various sectors of the financial market, government officials and economic regulators. These findings are of great importance to policymakers and stakeholders in global food systems, highlighting the need to create adapted policy frameworks, focus on the vulnerability of individual commodities, and carefully implement climate policies to mitigate potential negative impacts on food security.

Stock markets in the Middle East and North Africa face recurrent global shocks that can alter risk pricing and expected returns. This study assesses whether economic policy uncertainty, geopolitical risk, and climate policy uncertainty transmit to MENA equity returns. The objective is to estimate and compare the dynamic effects of global economic policy uncertainty (GEPU), geopolitical risk (GPR), and climate policy uncertainty (CPU) on stock market returns in the region and to identify the dominant uncertainty channel. Monthly data for ten MENA countries from January 2003 to August 2023 are analysed. Returns and uncertainty indices are expressed as log differences and checked for stationarity using ADF and PP tests. A structural vector autoregressive (SVAR) framework is estimated for each market, with lag length chosen by the Akaike criterion, and impulse responses and variance decompositions evaluate shock transmission. GEPU shocks exert statistically significant negative effects on returns for roughly four months in most markets, whereas Lebanon, Morocco, and Tunisia show weaker or insignificant responses. In contrast, GPR and CPU shocks do not produce statistically meaningful return effects. Variance decomposition indicates that GEPU contributes more to return variation than GPR or CPU, and its share generally increases with the forecast horizon, although own return shocks remain predominant. Findings support integrated risk monitoring for regulators and hedging strategies for investors. Future research should test nonlinear or regime switching dynamics, include volatility and capital flow channels, refine climate exposure measures, and assess transition period effects.

Investing in green bonds has been recognized as a strategy that not only promotes environmental performance but also offers attractive investment returns. However, macroeconomic factors, such as geopolitical risk (GPR), climate policy uncertainty (CPU), and global economic policy uncertainty (GEPU), play a substantial role in shaping green bond returns. Using the returns of US green bonds as a proxy for the returns of green bonds, we argue in this paper that these heterogenous uncertainty measures might have asymmetric impacts on the returns of green bonds (GB). By employing monthly data from January 2016 to August 2022, we apply the nonlinear autoregressive distributed lags model (NARDL) to examine the asymmetric impact of these heterogenous uncertainty measures on the returns of GB. The NARDL findings reveal evidence of short‐run asymmetric impacts of GPR, GEPU, and CPU on the returns of green bonds. In the long run, there is an asymmetric impact of GPR and CPU on the returns of GB. However, there is a symmetric impact of CPU on the returns of GB in the long run. Specifically, in the short run, we found that a positive CPU shock causes a decline in the returns of green bonds. A similar magnitude of negative CPU shock causes an increase in the returns of green bonds. Moreover, a positive shock to GPR increases the return of green bonds, while a comparable negative shock to GPR reduces the return of GB. Furthermore, only negative shocks to GEPU have an impact on GB returns in the short run. Specifically, a negative GEPU shock reduces GB returns. In the long term, the returns of GB are positively impacted by negative shocks in GPR and both positive and negative shocks in GEPU, whereas positive shocks in GPR affect the returns of GB negatively.

In the present study, it was explored how the volatility of the carbon neutrality concept index (CNCI) was affected by China economic policy uncertainty (CEPU) index, climate policy uncertainty (CPU) index, and geopolitical risk (GPR) index. According to the research of Amendola et al. the GARCH-MIDAS model was improved by introducing the realized kernel volatility of China stock market into the short-term volatility component. On this basis, the GARCH-RKV-MIDAS model was constructed. Meanwhile, both GARCH-MIDAS and GARCH-RKV-MIDAS models were applied to identify the influencing factors for CNCI volatility during the period between January 2018 and June 2022, with CNCI predicted. According to the research results, both the CPU index and the GPR index exert a significant effect on the long-term volatility of CNCI, despite no significant difference made by the CEPU index to the long-term volatility of CNCI. As for the prediction of CNCI volatility, the GARCH-RKV-MIDAS model clearly outperforms the GARCH-MIDAS model. Moreover, the CPU index outperforms the GPR index and the CEPU index in predicting the volatility of the CNCI.

Quantile connectedness between the climate policy and economic uncertainty: Evidence from the G7 countries

ABSTRACTThis study examines the role of the US climate policy uncertainty (CPU) index in forecasting the equity premium, employing shrinkage methods such as LASSO and elastic net (ENet) to dynamically select predictors from a dataset spanning April 1987 to December 2022. Alongside CPU, other uncertainty predictors like economic policy uncertainty (EPU), geopolitical risk (GPR), and the volatility index (VIX) are considered to assess their complementary roles in out‐of‐sample (OOS) equity premium forecasting. The results reveal that while CPU alone cannot consistently predict the equity premium, it provides crucial complementary information when combined with other predictors, leading to a statistically significant OOS of 1.231%. The relationship between CPU and the equity premium is time varying, with a stronger influence observed during periods of economic downturn or heightened uncertainty, as demonstrated by wavelet coherence analysis. This study also identifies CPU's significant impact on industry‐specific returns, particularly in climate‐sensitive sectors, offering valuable insights for investment strategies and risk management in an era of increasing CPU.

Type of the article: Research ArticleAbstractThis study aims to empirically examine the dynamic effects of geopolitical risk, economic policy uncertainty, and climate policy uncertainty on CO₂ emissions in G7 economies, utilizing annual data from 1990 to 2022. To account for cross-sectional dependence and parameter heterogeneity, the analysis employs a cross-sectional autoregressive distributed lag (CS-ARDL) model. Diagnostic tests confirm significant cross-sectional dependence and slope heterogeneity among the variables. All variables are integrated of order one, I (1), confirmed by unit root tests. In contrast, the cointegration test provides a strong indication of a stable long-run relationship among geopolitical risk, policy uncertainty measures, and CO₂ emissions. The outcomes show that a 1% rise in the geopolitical risk index leads to a statistically significant long-run rise of 0.042% in per capita CO₂ emissions. In addition, a 1% increase in economic policy uncertainty and climate policy uncertainty is associated with long-run increases of 0.028% and 0.015%, respectively. These results remain robust across alternative estimators. Overall, the evidence suggests that heightened geopolitical risk and policy-related uncertainties significantly exacerbate environmental degradation in G7 economies, highlighting the necessity for strategies that improve stability, reduce uncertainty, and encourage renewable energy adoption as part of a long-term environmental strategy.

Given the significance of fostering sustainable climate conditions for long-term economic stability and financial resilience, this study probes the connection between climate-related policy ambiguity and its implications for currency valuation. In doing so, the current study investigates the interconnected effects of climate policy on economic policy uncertainty and geopolitical risk with the currency valuation in ASEAN countries. Employing wavelet coherence analysis and partial wavelet coherence analysis, the paper highlights the complex relationships among these factors and their implications for exchange rate fluctuations. Using data from 2000 to 2022, the findings reveal that climate policy uncertainty is an important driver of exchange rate movements, amplifying the impact of economic policy uncertainty and geopolitical risk. Furthermore, the study identifies a vicious cycle between climate policy uncertainty and exchange rates, potentially impacting the region's macroeconomic stability and long-term economic growth. The study presents several policy recommendations to address economic and climate policy uncertainties comprehensively based on the findings. These recommendations include establishing national frameworks for climate risk management, enhancing policy credibility and macroeconomic stability, and promoting regional integration to mitigate the influence of geopolitical risk on exchange rates.

Purpose The term environmental, social and governance (ESG) has gained momentum in recent years. Thus, understanding its underlying driving mechanisms has become increasingly intriguing. In this study, we examine the effects of economic policy uncertainty (EPU), climate policy uncertainty (CPU) and geopolitical risk (GPR) on firms’ ESG disclosure. Design/methodology/approach To achieve the paper’s goal, we use the mixed data sampling (MIDAS) regression model on a sample of 500 firms from the US stock market. Findings The results indicate that EPU and GPR have significant adverse effects on ESG performance, whereas the CPU index exhibits a positive impact. However, the significant associations of CPU and GPR with ESG score observed in the whole sample do not hold consistently across industries. This suggests that, while overarching trends might exist, individual sector characteristics and varying external influences can shape the relationship between uncertainty factors and ESG performance. This highlights the importance of context when analyzing ESG practices across different industries. Furthermore, we identified a range of influential financial parameters that drive ESG practices. Practical implications The findings of our study have significant implications for many parties including policymakers, managers, governments, investors and shareholders. Additionally, sector-specific policies may be needed to encourage sustainable practices, particularly in industries sensitive to environmental and geopolitical risks. Originality/value The paper’s originality lies in its examination of the combined impact of EPU, CPU, and GPR on ESG disclosure – a relationship rarely explored in existing literature. Using the MIDAS model, it provides a more refined analysis by integrating both high- and low-frequency data. The study also offers industry-specific insights, revealing how these external uncertainties affect ESG disclosure differently across sectors. By incorporating financial drivers and offering policy implications, this research presents a novel and comprehensive approach to understanding ESG disclosure under increasing global uncertainties.

Spillover effects of climate policy uncertainty on green innovation.

Predicting tourism demand is crucial for policymakers and stakeholders in the tourism industry, enabling them to enhance promotional strategies, optimize marketing efforts, align investments, and achieve operational efficiency. While existing research has highlighted the negative impact of global risk factors such as geopolitical risks, economic policy uncertainty, and climate changes on tourism demand, the specific influence of climate policy changes and their differential impact on tourist arrivals has been less explored. Addressing this gap, our study employs bivariate, multiple, and partial wavelet methods to analyze the differential impact of climate policy uncertainty and geopolitical risk on tourism arrivals in the United States. Utilizing recent monthly data from January 1996 to August 2022, we uncover significant time-frequency varying dependencies between these factors and tourism demand. Our causality analysis reveals that both geopolitical risk and climate policy uncertainty are key determinants of tourism demand in the U.S. Furthermore, the Nonlinear Autoregressive Distributed Lag model corroborates the findings of the wavelet analysis, highlighting asymmetrical interactions in the long run. These findings offer valuable insights and have significant implications for policymakers and industry stakeholders, suggesting the need for adaptive and responsive tourism strategies that consider the evolving landscape of geopolitical and climate policy uncertainties.

This study examines the dynamic impact of the U.S.–China trade war and geopolitical risks on African stock market returns. Using Wavelet Coherence analysis and the Quantile Vector Autoregression (QVAR) model, we capture both time–frequency dynamics and regime-specific connectedness. Drawing on data from seven major African stock exchanges, Geopolitical Risk (GPRI) and U.S.–China Trade Tension (UCTI) indices from January 2007 to February 2024, the results reveal that African markets are not immune but exhibit state-dependent vulnerability. During calm market conditions, trade tensions dominate as the main shock transmitter, whereas geopolitical risks become more influential in crisis periods. The Johannesburg Stock Exchange (JSE) emerges as a key transmitter of shocks, while the Nigerian Exchange (NGX) remains the largest receiver. These findings underscore the need for targeted regional risk management and coordinated policy responses to enhance Africa’s financial resilience against external shocks.

The time-varying impact of uncertainty on oil market fear: Does climate policy uncertainty matter?

PurposeThis paper aims to investigate the effect of the economic policy uncertainty (EPU), geopolitical risk (GPR) and climate policy uncertainty (CPU) of USA on Bitcoin volatility from August 2010 to August 2022.Design/methodology/approachIn this paper, the authors have adopted the empirical strategy of Yen and Cheng (2021), who modified volatility model of Wang and Yen (2019), and the authors use an OLS regression with Newey-West error term.FindingsThe results using OLS regression with Newey–West error term suggest that the cryptocurrency market could have hedge or safe-haven properties against EPU and geopolitical uncertainty. While the authors find that the CPU has a negative impact on the volatility of the bitcoin market. Hence, the authors expect climate and environmental changes, as well as indiscriminate energy consumption, to play a more important role in increasing Bitcoin price volatility, in the future.Originality/valueThis study has two implications. First, to the best of the authors’ knowledge, the study is the first to extend the discussion on the effect of dimensions of uncertainty on the volatility of Bitcoin. Second, in contrast to previous studies, this study can be considered as the first to examine the role of climate change in predicting the volatility of bitcoin. This paper contributes to the literature on volatility forecasting of cryptocurrency in two ways. First, the authors discuss volatility forecasting of Bitcoin using the effects of three dimensions of uncertainty of USA (EPU, GPR and CPU). Second, based on the empirical results, the authors show that cryptocurrency can be a good hedging tool against EPU and GPR risk. But the cryptocurrency cannot be a hedging tool against CPU risk, especially with the high risks and climatic changes that threaten the environment.

This paper examines the impact of various uncertainty channels on stock market returns in Saudi Arabia, with a focus on the Tadawul All Share Index (TASI). It examines factors such as Saudi-specific Geopolitical Risk, Global Oil Price Uncertainty, Climate Policy Uncertainty, and U.S. Monetary Policy Uncertainty. Using monthly data from November 1998 to June 2024 and the Local Projections (LP) methodology, the study examines how these uncertainties impact market returns across various time horizons, taking into account potential structural breaks and nonlinear dynamics. Our findings indicate significant variations in the market’s response to the uncertainty measures across two distinct periods. During the first period, geopolitical risks have a strong positive impact on market returns. Conversely, the second period reveals a reversal, with negative cumulative effects, suggesting a shift in risk–return dynamics. Oil Price Uncertainty consistently exhibits a negative impact in both periods, highlighting the changing nature of oil dependency in the Saudi market. Additionally, Climate Policy Uncertainty is becoming more significant, reflecting increased market sensitivity to global environmental policy changes. Our analysis reveals significant asymmetries in the effects of various uncertainty shocks, with Monetary Policy Uncertainty exhibiting nonlinear effects that peak at intermediate horizons, while commodity-related uncertainties exhibit more persistent impacts. These findings, which remain robust across various tests, offer critical insights for portfolio management, policy formulation, and risk assessment in emerging markets undergoing substantial economic changes.