Research Articles (Economics)

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

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Do shortages forecast aggregate and sectoral U.S. stock market realized variance? Evidence from a century of data
Bonato, Matteo; Gupta, Rangan; Pierdzioch, Christian (Elsevier, 2026-04)
Recent global economic and political events have made clear that shortages are a key factor driving macroeconomic and financial market developments. Against this backdrop, we studied the forecasting value of shortages for monthly U.S. stock market realized variance (RV) at the aggregate and sectoral level using data spanning the period 19002024 and 19262023 (for most sectors), respectively. To this end, we considered linear and non-linear statistical learning estimators. When we used linear estimators (OLS and shrinkage estimators), we did not find evidence that aggregate and disaggregate shortage indexes have predictive value for subsequent market or sectoral RVs. In contrast, when we used random forests, a nonlinear nonparametric estimator, we detected that aggregate and disaggregate shortage indexes improve forecast accuracy of market and sectoral RVs after controlling for realized moments (realized leverage, realized skewness, realized kurtosis, realized tail risks). We then decomposed RV into a high, medium, and low frequency component and found that the shortages indexes are correlated mainly with the medium and low frequencies of RV. Finally, we found that the predictive value of shortages for RV was larger in the 1980s and 1990s than in later parts of our sample period. HIGHLIGHTS • Uses shortages to forecast aggregate and sectoral U.S. stock market realized variance. • Studies data spanning 19002024 and 19262023 (for most sectors). • Estimates random forests to recover forecasting value of shortages. • Controls for realized moments and other common predictors. • Predictive value of shortages has decreased in later parts of the sample period.
Return-volatility nexus in the digital asset class : a dynamic multilayer connectedness analysis
Bouri, Elie; Foglia, Matteo; Karmakar, Sayar; Gupta, Rangan (Wiley, 2026-04)
Based on the rationale that returns and volatility are interrelated, we apply a multilayer network framework involving the return layer and volatility layer of cryptocurrencies, NFTs, and DeFi assets over the period January 1, 2018–January 23, 2024. The results show significant connectedness in each of the return and volatility layers, with major cryptocurrencies such as Bitcoin and Ethereum playing a central role. Large spikes in the level of connectedness are noticed around COVID-19 pandemic and Russia–Ukraine conflict, and Bitcoin and Ethereum emerge as net transmitters of returns and volatility shocks, emphasizing their significant role around these crisis periods. Notably, a strong positive rank correlation exists between the return and volatility layers, highlighting the significant risk–return relationship in the digital asset class. The findings suggest that economic actors should not ignore the interconnectedness between the return and volatility layers in the system of cryptocurrencies, NFTs, and DeFi assets for the sake of a comprehensive analysis of information flow. Otherwise, a share of the information flow concerning the return–volatility nexus across these digital assets would be missed, possibly leading to inferences regarding asset pricing, portfolio allocation, and risk management.
Sustainability uncertainty and the stock market volatility in advanced and emerging markets : the role of the oil orientation of countries
Salisu, Afees A.; AbdulHakeem, AbdulHameed; Raddaoui, Mounira (Elsevier, 2026-06)
We provide new evidence on the relationship between sustainability uncertainty and stock market volatility, focusing on two channels of transmission: level of development and oil orientation. We categorize markets into advanced and emerging economies, and oil-producing and non-oil-producing countries. Our findings show that the relationship varies significantly. Oil-producing countries exhibit a notable sensitivity to sustainability-related uncertainty, particularly during heightened uncertainty, while emerging markets respond more to it, seeking higher expected returns. In contrast, developed markets show weaker or opposing reactions. We suggest that empirical research should consider countries' oil orientation and the level of development when analyzing climate-related shocks in financial markets. Our results highlight the need for distinct market classifications and underscore the importance of market development in shaping responses to sustainability uncertainty, advocating coordinated global efforts to reduce it.
Which money to follow? Evaluating country-specific vulnerabilities to illicit financial flows
Grondona, Verónica; Meinzer, Markus; Monkam, Nara F.; Schultz, Alison; Villanueva, Gonzalo (Springer, 2025-12)
This study presents a new, multidisciplinary method to assess countries’ vulnerabilities to illicit financial flows (IFFs) in different economic channels. Acknowledging that money laundering involves legitimate financial channels and regulatory gaps, our approach combines quantitative data on bilateral economic activities with a qualitative assessment of the regulatory frameworks of trade and investment partners. Using publicly available and contemporary data along with a legal analysis focused on the loopholes that can be exploited for IFFs, the proposed methodology addresses the limitations of current National Risk Assessments (NRAs) and offers an accessible and cost-effective approach that can be applied for anti-money laundering. We illustrate the effectiveness of our approach by analyzing IFF vulnerabilities in Nigerian inward foreign direct investment, Brazilian outward portfolio investment, and Indonesian imports demonstrating its potential to refine and enhance National Risk Assessments.
Financial development and trade openness in Sub-Saharan Africa : linear and non-linear modelling approaches
Unah, Innocent Ogbonnaya; Olaniran, Abeeb Olatunde; Lasisi, Lukman Abisoye (Emerald, 2026)
PURPOSE : This study investigates the linear and nonlinear effects of financial development on trade openness in Sub-Saharan Africa. DESIGN/METHODOLOGY/APPROACH : This study focuses on SSA and its sub-regions over the period 1990–2022 to investigate both linear and non-linear relationships between financial development and trade openness. While the former utilises CS-ARDL, the latter is captured within a panel threshold model. FINDINGS : We find overarching evidence that financial development enhances trade openness in Southern and Central Africa, with effects evident in both the short and long run for Southern Africa. Threshold analysis, however, provides only weak evidence of non-linearity across SSA and its sub-regions. RESEARCH LIMITATIONS/IMPLICATIONS : The main limitation of this study is its coverage, which ends in 2022 and therefore does not capture the potential impact of more recent global disruptions – such as ongoing geopolitical and climate risk events – on the relationship between financial development and trade openness in Africa. Nonetheless, the findings contribute a fresh perspective by examining this nexus through the lens of asymmetries, offering insights that can guide future research and policy discussions. PRACTICAL IMPLICATIONS : The findings carry important implications for trade policy, highlighting the need for policymakers in SSA to deepen financial systems while at the same time strengthening institutional frameworks, trade infrastructure, and industrial capacity. ORIGINALITY/VALUE : This study highlights the threshold effects of financial development, suggesting that complementary measures are necessary for financial development to become a stronger driver of trade openness across the African continent. In doing so, it contributes to the broader discourse on how financial development fosters regional and global integration through trade openness.
The impact of biomass energy and information and communication technology on economic growth : a global panel data analysis
Majeed, M. Tariq; Bolat, Suleyman; Inglesi-Lotz, Roula; Tiwari, Aviral Kumar (Taylor and Francis, 2026)
As digital technologies expand and renewable energy becomes a priority in climate and economic development policy, countries are pursuing strategies and approaches to promote sustainability in their economic growth trajectories. This study explores the dynamic relationship between biomass energy consumption, information and communication technology (ICT), and economic growth within a global context. Motivated by the increasing role of digitalization and renewable energy in shaping sustainable economic pathways, this research investigates whether these factors serve as catalysts for economic expansion. Utilising panel data from 157 countries spanning 1990 to 2023, we employ an ICT index incorporating internet users, fixed broadband subscriptions, mobile cellular subscriptions, and fixed telephone subscriptions. To ensure robustness, the empirical methodology integrates fixed and random effects models, and system GMM estimation. We further employ panel quantile regression analysis to account for potential slope heterogeneity. The findings underscore that biomass energy consumption significantly fosters economic growth, though its impact diminishes in high-growth economies. In contrast, ICT emerges as a strong and consistent driver of economic growth across all levels of economic development. These insights offer critical policy implications, highlighting the need for tailored strategies that leverage digital transformation and sustainable energy sources to optimize economic outcomes.
Forecasting natural gas futures price volatility of the United States : national versus state-level climate concern indexes
Salisu, Afees A.; Ogbonna, Ahamuefula E.; Gupta, Rangan; Polat, Onur (Wiley, 2026)
This paper uses GARCH-MIDAS to predict US natural gas futures volatility using national and state-level Climate Concern Indexes (CCIs). We find that both national and state-level CCIs positively affect price volatility. Notably, models using state-level data—specifically those utilizing least-squares (LS) weighting combinations—surpass the GARCH-MIDAS-GECON benchmark and models relying solely on national CCI. These findings deliver substantial statistical and economic utility gains. Our results underscore the importance of incorporating heterogeneous climate concerns across US states to capture varied demand-supply conditions when forecasting energy market volatility.
The connectedness between international energy-related uncertainties, economic policy uncertainties, and clean energy markets
Inglesi-Lotz, Roula; Venter, Alanda; Tzeremes, Panayiotis (Taylor and Francis, 2026)
Energy is an essential element of economic development. In maintaining a stable energy supply, countries worldwide are facing a transition towards cleaner energy sources and technologies. While gains in the usage and promotion of cleaner energy have been made in this pursuit, the transition toward cleaner energy faces obstacles concerning energy-related uncertainties and economic policy uncertainties, hindering investor confidence. This study explores the connectedness between a key element in the energy transition—clean energy stock returns, the energy-related uncertainty index, and the economic policy uncertainty index, using monthly data from 2005 to 2022. In using a quantile spillover approach to assess the dynamic relationship between the variables, the results indicate that in times of volatility within the market, the connectedness between clean energy returns, energy-related uncertainty, and economic policy uncertainty increases. In a less volatile market environment, the results, however, yield that clean energy returns, energy-related uncertainty, and economic policy uncertainty interdependency increase. These results underscore the need to create clean energy investment strategies and informed policy responses amid an increasingly volatile market and geopolitical tensions.
Time-varying multilayer networks analysis of frequency connectedness in commodity futures markets
Zhou, Xuewei; Ouyang, Zisheng; Gupta, Rangan; Ji, Qiang (Springer, 2026-01)
This paper constructs multilayer frequency networks containing short-, medium-, and long-term layers to examine the frequency connectedness among commodity futures markets. We examine the frequency heterogeneity of commodity volatility connectedness at the average, dynamic, and crisis levels. We also investigate the determinants of frequency connectedness among commodity futures markets. The results show that there are strong short-term volatility spillovers between commodity futures markets, while connectedness during crises is dominated by long-term factors. We find that there is heterogeneity in the edge structure of short- and long-term networks during the crisis. In addition, we note that cocoa futures can hedge frequency risk in commodity markets. Determinants analysis suggests that inflation risk is the key driver of frequency connectedness in commodity futures. Moreover, the drivers of connectedness differ between short, medium, and long terms. Our work provides new insights for studying the risk contagion of commodity markets and informs the decisions of investors and regulators.
Advancing context-specific urban indicators for African cities : a systematic review
Afinowi, Taiwo; Monkam, Nara F. (Nature Research, 2025-11-27)
This study highlights the limitations of generic urban assessment frameworks in evaluating the sustainability of African cities and the Global South. It argues for the development of context-specific indicators that accurately reflect the unique socio-economic, cultural, spatial, and historical realities of these cities. While global frameworks offer useful benchmarks, their standardised methodologies often overlook the structural characteristics that define African cities, namely, informality, spatial and economic inequalities, governance constraints, and colonial urban legacies. To investigate why African cities frequently underperform when assessed using global urban sustainability indicators, we conducted a systematic literature review following the PRISMA framework. Our findings underscore the need to rethink assessment frameworks by expanding sustainability dimensions. We propose integrating underexplored yet critical sustainability dimensions and a novel ACTPL design conceptual framework to guide locally grounded sustainability metrics. This framework offers practical guidance for all urban stakeholders seeking to advance more inclusive and adaptive sustainable urban development strategies across the continent.
Artificial intelligence's (AI's) role in enhancing tax revenue, institutional quality, and economic growth in selected BRICS-plus countries
Saba, Charles Shaaba; Monkam, Nara F. (Springer, 2026)
The BRICS countries, comprising Brazil, Russia, India, China, and South Africa, aim to achieve United Nations Sustainable Development Goal 8, which emphasizes sustainable economic growth. This study adds to the empirical literature by examining the impact of tax revenue and institutional quality on economic growth, incorporating the role of artificial intelligence (AI) in selected BRICS-Plus countries (the above-mentioned five countries) from 2012 to 2022. Utilizing the innovative Cross-Sectional Augmented Autoregressive Distributed Lag estimation technique, the analysis reveals a long-run equilibrium relationship among the variables. The study employs a modified Cobb–Douglas production function for its theoretical framework. The results indicate bidirectional causality between tax revenue and AI, economic growth and institutional quality, as well as institutional quality and tax revenue. Based on these findings, the study recommends that BRICS governments and policymakers enhance the integration of AI into tax systems to promote growth in both the short and long terms. However, it also advises caution regarding the interaction between AI and institutional quality, which did not support economic growth. While the AI and tax revenue interaction shows promise for fostering growth, robust measures are necessary to mitigate potential negative effects from AI’s interaction with institutional quality. Consequently, the study advocates for the development of AI-friendly institutional policies in BRICS countries, considering the dynamic and rapidly evolving AI sector.
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.

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