Search Results (107)

The interplay between government economic policy uncertainty (EPU) and bank risk remains a key concern in the financial stability literature. This study advances the field by examining the dynamic, time-varying impact of EPU on bank risk, explicitly differentiating between short- and long-term effects. We posit a dual hypothesis: heightened EPU increases short-run bank risk by raising borrower default probabilities while decreasing long-run risk as banks adopt more conservative lending strategies, given the option value of waiting under high uncertainty. Analyzing bank-level data across 22 countries from 1998 to 2017, we find robust empirical support: EPU exerts an immediate positive effect on bank risk and a significant negative effect with a lag of two to four years. These findings are robust to endogeneity and multiple sensitivity checks. Our results explicitly demonstrate the dual role of policy uncertainty in shaping bank risk-taking and offer timely guidance for the design of regulatory and macroprudential frameworks. Full article

Technological innovation in energy enterprises constitutes a pivotal component in realizing the transition to a green economy. In recent years, the complexity and volatility of the international economic landscape have significantly amplified economic policy uncertainty (EPU) across nations, which is poised to exert a profound influence on the technological innovation activities of energy enterprises. This study employs the Tobit regression method to investigate the relationship between EPU and corporate technological innovation (CTI), based on data from Chinese listed energy companies spanning the period of 2007 to 2018. Empirical results indicate that EPU exerts a significant positive influence on technological innovation for energy enterprises. Furthermore, we employed a Fisher permutation test to further elucidate the heterogeneity of this impact across various sub-industries, enterprise ownership types, and governance mechanisms. Specifically, EPU has a more pronounced promoting effect on technological innovation for traditional energy enterprises, non-state-owned enterprises, and enterprises with high failure tolerance. Against the backdrop of increasing global EPU, the findings of this study offer certain implications for governmental industrial policies and corporate governance mechanisms. Full article

Recent studies have focused on the detrimental effects of global economic policy uncertainty (EPU) on firm profitability. Nevertheless, none of these studies has focused on a developing economy like Nigeria. To understand this, the study conducted a host of regression analyses using the Driscoll and Kraay fixed-effect estimator and the two-step system generalised method of moments to examine the effects of global crude oil prices and domestic and global economic policy uncertainty on firm profitability in Nigeria from 2005 to 2024. The findings indicate that while global EPU had a minimal impact on firm profitability, domestic EPU had a substantial negative impact. The findings remain consistent even across the sub-samples, sensitivity, and robustness analyses. Furthermore, the findings showed that firm size and capital are significant determinants of profitability for Nigerian firms. At the same time, oil prices and their interactions do not affect firm profitability in Nigeria. The study suggests that regulators in the Nigerian business environment can contribute to building a more resilient environment by implementing systems to monitor critical economic indicators and ensure timely responses to emerging challenges. Systematic evaluations of economic uncertainties, including business sentiment, inflation rates, exchange rates, interest rates, and economic growth, can provide valuable insights for policy formulation and interventions aimed at enhancing the profitability of Nigerian firms. Full article

Purpose: This paper draws on the Upper Echelon Theory and the Agency Theory to explore a special aspect of managers’ behavioral characteristics—managerial myopia—as a driving factor in firms’ ESG performance, a key metric for sustainable development. This study utilizes a sample of Chinese A-share listed firms from 2010 to 2021. It integrates data from the China Stock Market and Accounting Research (CSMAR) database, the Wind database for corporate ESG performance, a managerial myopia index constructed through text analysis, machine learning, and dictionary methods, internal control data from the DIB Internal Control Database, and the Economic Policy Uncertainty (EPU) index. The study examines the relationship between managerial myopia and ESG performance and explores the moderating effects of internal control, corporate transparency, and EPU. This study finds that managerial myopia significantly impedes corporate sustainability by significantly negatively impacting ESG performance. This finding underscores a critical challenge to sustainable development: short-term managerial orientation can compromise long-term environmental and social goals. However, robust internal governance mechanisms, such as effective internal control and high corporate transparency can mitigate this negative impact, while higher EPU exacerbates it. Additionally, the detrimental effect of managerial myopia is more pronounced in firms with higher business complexity, smaller firm size, and state-owned enterprises (SOEs). This paper suggests that, in addition to demographic characteristics and management experience, corporate governance and hiring practices should consider managers’ temporal orientation to foster sustainable business practices. Firms should focus on establishing a robust internal control environment and increasing corporate transparency to safeguard long-term sustainability objectives from short-sighted managerial behavior, especially in situations of high economic policy uncertainty and in organizations with higher business complexity, smaller firm size, and SOEs. The main limitations of this study include the lack of analysis on the influencing mechanisms and not fully addressing the endogenous problem, and the international generalizability of the finding should be further expanded in future. This paper contributes to sustainability science by extending the current literature focusing on the behavioral drivers of firms’ ESG performance, emphasizing the under-explored role of managerial myopia. This provides meaningful insight into the role of executive characteristics in shaping corporate sustainability, particularly in emerging market contexts. It also adds value by identifying how internal governance and external environmental factors condition this relationship, offering insights for policymakers and corporate leaders aiming to advance sustainable development. Full article

Major events and external uncertainty shocks have made energy risk connectedness increasingly complex. This paper applies a LASSO-regularized VAR combined with the Diebold-Yilmaz connectedness framework (LASSO-VAR-DY) to trace how China’s energy risk spillover effects evolve under major event shocks and to quantify sectoral risk spillover inflows. We then employ a TVP-SV-VAR model to further examine the impulse responses of energy sectors to external uncertainties. The results show that the energy system exhibits a high overall level of risk connectedness with pronounced stage-wise variation and is sensitive to different external uncertainty shocks. Major-event shocks intensify sector-level risk connectedness—the clean-energy sector consistently acts as a net risk receiver. In contrast, other sectors switch between net transmitters and net receivers across shocks. Different major events operate through heterogeneous mechanisms—the COVID-19 pandemic and the official launch of the national carbon market primarily strengthen node-to-node connectedness. In contrast, the Russia-Ukraine conflict chiefly amplifies spillover intensity between nodes. The effects of uncertainty index shocks differ markedly: economic policy uncertainty (EPU) has the most substantial impact, followed by climate policy uncertainty (CPU), while geopolitical risk (GPR) is the weakest. Full article

This study examines the intricate relationships between cryptocurrency and various uncertainties related to economic policy and global risk factors. It explores the interactions between cryptocurrency and global risk factors, comparing these with their relationships to different measures of economic policy uncertainty (EPU). We find that cryptocurrency returns are more sensitive to global risk factors than to the country-level EPU. Notably, gold exhibits bidirectional causality with cryptocurrency in returns and volatility. The research sheds light on the dynamic interactions within cryptocurrency markets, underscoring the importance of continuous monitoring and adaptive strategies to navigate the evolving financial landscape of the digital ecosystem. Full article

This study examines risk transmission between the United States and China using integrated economic policy uncertainty (EPU) and geopolitical risk (GPR) indices. We employ a dual methodology that combines Vector Autoregressive (VAR) and Granger causality in quantiles tests to analyze interactions during systemic leadership transitions, a dimension that is currently under-explored. Our dataset covers the period from June 2000 to June 2023. Results indicate that China is narrowing the economic influence gap and strengthening its role as a regional anchor. The U.S., however, maintains predominant global leadership. This dynamic reframes bilateral tensions as a “status dilemma” rather than a security conflict. Crucially, we identify asymmetric spillover effects: the U.S. uncertainty shocks spread globally, while China’s volatility remains regional. Our findings contribute to the understanding of financial stability by demonstrating that leadership asymmetries are critical determinants, providing valuable insights for designing systemic risk monitoring tools and contagion mitigation policies during periods of heightened uncertainty. Full article

This study examines the dynamics of equity market volatility and economic policy uncertainty (EPU) in South Africa during the COVID-19 pandemic. Using daily return data for sectoral indices and the JSE All Share Index (ALSI) from 1 January 2020 to 31 March 2022, the analysis explores both market-wide and sector-specific volatility responses. Univariate GARCH-family models (GARCH (1,1), E-GARCH, and T-GARCH) are employed to capture volatility clustering, persistence, and asymmetry across sectors. The results show that volatility was highly persistent during the pandemic, with sectoral differences in sensitivity to shocks: Consumer Staples and Financials were particularly reactive to recent news, while Health Care and Basic Materials were more stable. Asymmetric models confirm that market sentiment was predominantly driven by negative news, except in the Energy sector, where positive recovery signals played a stronger role. Correlation analysis further indicates that most sectors were moderately correlated with the ALSI, while Energy and Health Care behaved more independently. In contrast, both the ALSI and sector returns exhibited weak and negative correlations with the South African EPU index, suggesting that uncertainty did not translate directly into equity market declines. Overall, the findings highlight the importance of sectoral heterogeneity in volatility dynamics and suggest that during extreme market events, investors can mitigate downside risk by reallocating portfolios toward more resilient sectors. Full article

This study investigates the nonlinear and regime-dependent relationship between climate policy uncertainty (CPU) and sovereign credit default swap (CDS) spreads across a panel of developed and emerging economies from February 2010 to March 2025. Utilizing the Quantile-on-Quantile Regression (QQR) and Multivariate QQR (MQQR) frameworks, we capture the heterogeneous effects of CPU under varying market states and assess the marginal role of global risk factors, including geopolitical risk (GPR), economic policy uncertainty (EPU), and market volatility (VIX). The findings indicate that in developed markets, CPU exerts a nonlinear impact that intensifies during periods of heightened sovereign risk, while in low-risk regimes, its effect is often muted or reversed. In contrast, emerging economies exhibit more volatile and state-contingent responses, with CPU exerting stronger effects in calm conditions but diminishing in explanatory power once global risks are taken into account. These dynamics highlight the importance of institutional credibility and financial integration in moderating CPU-driven credit risk. Full article

This study investigates whether firm-specific narratives extracted from the news add predictive content to monthly stock return models. Using bidirectional encoder representations from transformer-based topic modeling (BERTopic), we processed Microsoft (MSFT) news and constructed monthly narrative activations (binary presence and decay weighting). These narrative activations are used in autoregressive moving-average models with exogenous regressors (ARIMA-X) to analyze MSFT monthly log returns alongside the U.S. Economic Policy Uncertainty (EPU) index from February 2021 to March 2025. Decay models using a similarity-distilled BERT embedding yielded three significant narratives: Media and Public Perception (MPP) (β = 0.0128, p = 0.002), Currency and Macro Environment (CME) (β = −0.0143, p < 0.001), and Tech and Semiconductor Ecosystem (TSE) (β = −0.0606, p = 0.014). Binary activation identifies reputational shocks: the Media and Public Perception (MPP) indicator predicts lower returns at one- and two-month lags (β = −0.0758, p = 0.043; β = −0.1048, p = 0.007). A likelihood-ratio test comparing ARIMA-X models with narrative regressors to a baseline ARIMA (no narratives) rejects the null hypothesis that narratives add no improvement in fit (p < 0.01). Firm-level narratives enhance monthly forecasts beyond conventional predictors; decay activation and similarity-distilled embeddings perform best. Demonstrated on Microsoft as a proof of concept, the ticker-agnostic design scales to multiple firms and sectors, contingent on sufficient firm-tagged news coverage for external validity. Full article

Economic stability in emerging market economies is increasingly shaped by the interplay between global financial integration, domestic monetary dynamics, and asset price fluctuations. Yet, early detection of financial market disruptions remains a persistent challenge. This study constructs a Financial Stress Index (FSI) for Pakistan, utilizing monthly data from 2005 to 2024, to capture systemic stress in a globalized context. Using Principal Component Analysis (PCA), the FSI consolidates diverse indicators, including banking sector fragility, exchange market pressure, stock market volatility, money market spread, external debt exposure, and trade finance conditions, into a single, interpretable measure of financial instability. The index is externally validated through comparisons with the U.S. STLFSI4, the Global Economic Policy Uncertainty (EPU) Index, the Geopolitical Risk (GPR) Index, and the OECD Composite Leading Indicator (CLI). The results confirm that Pakistan’s FSI responds meaningfully to both global and domestic shocks. It successfully captures major stress episodes, including the 2008 global financial crisis, the COVID-19 pandemic, and politically driven local disruptions. A key understanding is the index’s ability to distinguish between sudden global contagion and gradually emerging domestic vulnerabilities. Empirical results show that banking sector risk, followed by trade finance constraints and exchange rate volatility, are the leading contributors to systemic stress. Granger causality analysis reveals that financial stress has a significant impact on macroeconomic performance, particularly in terms of GDP growth and trade flows. These findings emphasize the importance of monitoring sector-specific vulnerabilities in an open economy like Pakistan. The FSI offers strong potential as an early warning system to support policy design and strengthen economic resilience. Future modifications may include incorporating real-time market-based metrics indicators to better align the index with global stress patterns. Full article

This paper examines the changing role of emissions trading systems (ETSs) within the macro-financial framework of energy markets, emphasizing price dynamics and systemic spillovers. Utilizing monthly data from seven ETS jurisdictions spanning January 2021 to December 2024 (N = 287 observations after log transformation and first differencing), which includes four auction-based markets (United States, Canada, United Kingdom, South Korea), two secondary markets (China, New Zealand), and a government-set fixed-price scheme (Germany), this research estimates a panel vector autoregression (PVAR) employing a Common Correlated Effects (CCE) model and augments it with machine learning analysis utilizing XGBoost and explainable AI methodologies. The PVAR-CEE reveals numerous unexpected findings related to carbon markets: ETS returns exhibit persistence with an autoregressive coefficient of −0.137 after a four-month lag, while increasing inflation results in rising ETS after the same period. Furthermore, ETSs generate spillover effects in the real economy, as elevated ETSs today forecast a 0.125-point reduction in unemployment one month later and a 0.0173 increase in inflation after two months. Impulse response analysis indicates that exogenous shocks, including Brent oil prices, policy uncertainty, and financial volatility, are swiftly assimilated by ETS pricing, with effects dissipating completely within three to eight months. XGBoost models ascertain that policy uncertainty and Brent oil prices are the most significant predictors of one-month-ahead ETSs, whereas ESG factors are relevant only beyond certain thresholds and in conditions of low policy uncertainty. These findings establish ETS markets as dynamic transmitters of macroeconomic signals, influencing energy management, labor changes, and sustainable finance under carbon pricing frameworks. Full article

The contagious COVID-19 disease not only brought about a global health crisis but also a disruption in the global economy. The uncertainty levels regarding the impact of the disease increased volatility. This study analyses stock market volatility and Economic Policy Uncertainty (EPU) of South Africa (SA) with that of the United States of America (USA) and Brazil, Russia, India, and China (BRIC) during the COVID-19 pandemic. The study aims to analyse volatility spillovers from a developed market (USA) to emerging markets (BRIC countries) and also to examine the causality between EPU and stock returns during the COVID-19 pandemic. By employing the GARCH-in-Mean model from a sample of daily returns of national equity market indices from 1 January 2020 to 31 March 2022, SA and China are shown to be the most volatile during the pandemic. By using the diagonal Baba, Engle, Kraft, and Kroner (BEKK) model to analyse spillover effects, evidence of spillover effects from the US to the emerging countries is small but statistically significant, with SA showing the strongest impact from US market shocks. From the Granger causality test, Brazil’s and India’s equity markets are shown to be highly sensitive to changes in EPU relative to the other countries. Full article

This study examines the impact of economic policy uncertainty (EPU) on agricultural land conversion. Using a newspaper-based index of EPU and a comprehensive panel dataset covering 270 prefecture-level cities in China, we estimate a city fixed effects model to explore this relationship. Our results indicate that a one-standard-deviation increase in EPU leads to a 22.2% increase in the conversion of agricultural land to urban residential, commercial, and industrial uses. This finding suggests that the surge in EPU triggered by the global financial crisis accounts for approximately 45% of the increase in agricultural land conversion. The adverse effect on agricultural land preservation mainly stems from intensified fiscal pressures and heightened demands on local governments to meet economic growth targets. To address potential endogeneity concerns, we employ the one-period lagged U.S. EPU index and its temporal variations as an instrument for China’s EPU, leveraging cross-country spillover effects. Our instrumental variable estimates confirm the validity of the land conversion effect and its underlying mechanisms. Furthermore, we find that the effects of EPU are particularly pronounced in cities located in non-eastern China and those that depend heavily on fixed asset investment for local economic development. Finally, our analysis of potential policy interventions to mitigate EPU-induced agricultural land loss suggests that strengthening market-oriented reforms and reducing province-level quotas on agricultural land conversion can effectively offset the impact of rising EPU. Full article

Existing stock volatility forecasting models predominantly rely on same-frequency market data while neglecting mixed-frequency integration and face particular challenges in incorporating low-frequency macroeconomic variables that exhibit temporal mismatches with financial market dynamics. To address this limitation, this study develops a novel hybrid approach for stock market volatility forecasting, which synergistically combines a deep learning model (CNN-BiLSTM-Attention) with the GARCH-MIDAS model. The GARCH-MIDAS model can fully exploit mixed-frequency information, including daily returns, monthly macroeconomic variables, and EPU. The deep learning model can effectively capture both spatial and temporal patterns of multivariate time-series data, thus effectively improving prediction accuracy and generalization ability in stock market volatility forecasting. The results indicate that the CNN-BiLSTM-Attention model yields the most accurate forecasts compared to the benchmark models. Furthermore, incorporating additional predictors, such as macroeconomic indicators and the Economic Policy Uncertainty Index, also provides valuable information for stock market volatility prediction, notably enhancing the model’s forecasting effect. Full article