This study examines the asymmetric and state-dependent predictive connectedness between media climate-change concern and green cryptocurrency returns. Using daily data for the Media Climate Change Concern Index (MCCC) and five environmentally oriented cryptocurrencies (EOS, IOTA, Stellar [XLM], Tezos [XTZ], and Cardano [ADA]), we employ a Quantile-on-Quantile (QQ) connectedness framework to capture heterogeneity across market conditions. The results reveal a distinct U-shaped pattern: spillovers intensify at the distributional tails and remain weak around the median. Interpreted through investor-attention mechanisms, heightened climate-related media salience coincides with stronger tail spillovers, suggesting that potential diversification properties may be regime-dependent rather than unconditional. The findings contribute to the growing literature on climate-related information transmission and sustainable digital assets, and offer implications for ESG-oriented portfolio risk monitoring and climate-related financial oversight.
In recent years, the correlation among financial markets has increased due to the joint impact of unexpected events, leading to significant fluctuations in the energy market and subsequent cascading effects on related markets. This study employs the Diebold and Yilmaz and Baruník and Křehlík spillover indices to examine the spillover relationship between traditional energy market and non-ferrous metal market under event shocks, analyzed from both time and frequency domains. Static spillover indices across six market sectors are used the static index matrix, while total and net spillover indices are explored dynamically throughout the sample period. The analysis revealed that Brent crude oil and Rotterdam coal exhibit negative spillover effects on other markets and function as net risk-takers. Notably, the level of spillovers increased markedly during COVID-19 pandemic and the outbreak of the Russian-Ukrainian conflict. The robustness test corroborates these findings. These results provide valuable insights for regulators, decision-makers and investors to better understand markets linkages between and optimize investment decisions.
We build on the Arrow–Debreu general equilibrium model by Arnold (2023) which features SRI in the form of portfolio preferences. Using assumptions from the classical finance literature, that model is modified such that it allows for bankruptcies. Firm capital inputs are shown to be entirely independent of debt levels, which constitutes the standard corporate finance Modigliani-Miller theorem. As long as debt alterings create no new spanning opportunities and do not destroy old ones, this result extends to a general equilibrium version, implying constant consumption of all individuals. If financial markets are not complete, higher debt may create new spanning opportunities and thus enhance welfare. This result is particularly relevant if individuals constrain the set of firms the assets of which they are willing to hold due to SRI.
This study examines how oil price shocks shape macroeconomic outcomes in Gulf Cooperation Council (GCC) countries, where fiscal and external balances remain highly sensitive to global energy markets. While earlier research has often treated oil exporters as a homogeneous group, this paper demonstrates that institutional differences, particularly fiscal space and sovereign wealth buffers, significantly shape the transmission of oil shocks. The analysis is grounded in a theoretical framework integrating the resource curse, Dutch disease, fiscal stabilization, and sovereign wealth fund literatures, which together predict that institutional quality mediates oil shock transmission. Using annual data from 1980 to 2023, we estimate dynamic responses via Panel Vector Autoregression (PVAR), Generalized Method of Moments (GMM)based techniques, and regime-dependent local projection models. Prior to estimation, we conduct formal tests for cross-sectional dependence (Pesaran CD test), second-generation panel unit root tests (CIPS), and panel cointegration analysis (Westerlund, 2007), confirming strong cross-sectional dependence across GCC economies and a long-run cointegrating relationship between GDP growth and oil prices. The analysis reveals that countries with stronger fiscal positions or larger sovereign wealth funds experience more stable responses to oil price changes, with the SWF surplus panel providing the clearest evidence: high fiscal space economies accumulate sovereign buffers countercyclically while constrained economies draw them down. These asymmetries are particularly pronounced during oil busts. The findings support the insights of the IMF (2023) and the GCC Vision 2030 goals, showing that savings, fiscal rules, and strong institutions help reduce vulnerability to oil price shocks.
This article examines how the COVID-19 pandemic and the related policy response influenced dividend policy in the US banking sector. We used an unbalanced panel of 3,652 US commercial banks from Moody’s Orbis, covering 144,062 bank-quarter observations over 2015Q2–2025Q2 period. Dividend policy is captured by the dividend-to-equity ratio and by the propensity to pay dividends. Mean comparison tests show that dividend payments declined during the pandemic relative to the pre-COVID period, particularly at the bank level, and increased again in the post-COVID period. To explain these patterns, we estimate fixed-effects Tobit models and find that state-level economic support measures, as well as containment and health interventions, are followed by higher dividend payouts, with effects emerging after several quarters. Complementary logit estimates reveal similar patterns for the likelihood of paying dividends. In addition, unconditional quantile regressions show that these positive effects are not uniform across banks, but become stronger toward the upper part of the dividend-to-equity distribution, indicating that the impact of policy support is enhanced for banks already characterised by relatively higher payouts.
This paper compares the predictive roles of monetary policy and central bank information shocks in the formation of bubbles in West Texas Intermediate (WTI) oil prices. Using daily data from February 1990 to July 2025, positive and negative bubbles in the short-, medium-, and long-term horizons are first detected. Then, a nonparametric causality-in-quantiles framework is employed to assess predictability at different levels of oil bubbles. The results show that both shocks predict the entire conditional distributions of all bubble indicators. Central bank information shocks carry relatively stronger predictive power than monetary policy surprises. In addition, the causal effect of these two shocks is higher for negative bubbles than positive ones, especially in the short-term. These findings suggest that central bank information shocks matter more than central bank information shocks to oil-market investors and traders when trying to predict impending crashes and recoveries in the oil market.
This article develops an empirical multivariate extension of the random-exponent fractal logic in the reference univariate study (Frezza, 2018). The empirical target is the joint distribution of nine cross-asset ETF returns observed from 2012-03-01 to 2026-02-27, a span that covers the COVID-19 pandemic, the Russia-Ukraine invasion, the Israel-Hamas war and two major tariff shocks. The proposed specification combines a rolling local roughness proxy, a vector autoregression for that state, asset-level conditional scales and a multivariate t-copula dependence layer that is re-estimated at each rolling origin and held fixed over the forecast window. Relative to the closest univariate-style benchmark, namely the independent fractal specification, the proposed model reduces energy distance by 13.2%, 18.5% and 21.8% at the 32-, 64- and 126-day horizons, while cutting correlation RMSE by 48.3% at 64 days and 52.3% at 126 days. Paired-bootstrap inference shows that the gains over the independent fractal benchmark are stable for energy distance and correlation RMSE at all horizons, while co-exceedance gains are not statistically distinguishable once overlap among forecast windows is preserved. All strong benchmarks, including DCC-GARCH, are evaluated on the same 66-origin grid and with the same simulation budget. After within-family Holm correction, the proposed model reduces 1% equal-weight portfolio quantile error relative to the historical block bootstrap by 12.4%, 15.0% and 21.5% across the three horizons; DCC-GARCH retains significantly lower co-exceedance RMSE at all horizons and lower 32-day correlation RMSE. Neither 5% quantile loss nor joint VaR-ES loss establishes systematic dominance. Conditional-coverage tests confirm that the 5% equal-weight VaR of the proposed model passes where the independent fractal and t-copula-without-H benchmarks fail, and the proposed model also passes for risk parity where DCC-GARCH fails. These findings do not imply universal dominance over every multivariate competitor: t-copula, historical bootstrap and DCC-GARCH benchmarks remain competitive. The contribution is more specific. Jointly modeling local roughness and cross-asset dependence materially improves the natural multivariate scaling of the original univariate design in both joint-distribution fit and portfolio-risk calibration.
This study delves into the ramifications of incorporating various structural state alterations observed within macroeconomic datasets on the forecasting accuracy of macroeconomic variables. To explore the existence of mechanism transitions, we developed a Smooth Transition Three-Pass Regression Filter (STR-3PRF) model based on the foundation of the three-pass regression filter. The extended model can capture dynamic changes and mechanism shifts within the economic environment. Empirical results indicate that high-dimensional datasets provide effective predictive information for macroeconomic variables. Furthermore, the STR-3PRF model captures the dynamic non-linear relationships between the macroeconomic dataset and macroeconomic variables. Noteworthily, comparative assessments reveal the STR-3PRF model’s significantly superior predictive efficacy relative to alternative models. Additionally, the STR-3PRF model has passed various robustness tests. Overall, our findings affirm the profound influence of regime transitions in macroeconomic datasets on the predictability of macroeconomic variables.
The improvement of firms’ total factor productivity (TFP) constitutes a core engine driving China’s high-quality economic transformation. This paper examines the relationship between bank digital transformation and firm-level total factor productivity, drawing on loan-level information from firms listed on China’s A-share market during the period 2010–2023. The results show that bank digital transformation is significantly and positively associated with firm-level TFP. Further analyses provide evidence consistent with several potential channels through which bank digital transformation is related to firm-level TFP, including credit availability, credit structure, and managerial efficiency. These effects are particularly pronounced among smaller firms, non-state-owned enterprises, and firms extensively engaged in earnings management. Additional evidence suggests that bank digital transformation mitigates constraints imposed by weak regional institutions and limited financial development, thereby facilitating TFP improvements in less developed markets. Overall, the findings underscore the role of bank digital transformation in fostering firm productivity and supporting high-quality growth.
The intensifying global climate crisis has rendered the role of sustainable finance instruments, particularly green assets, within financial systems increasingly significant. This study aims to examine the dependence structure among sustainable financial instruments across the United States, Europe, and Asia, thereby offering findings that may inform the development of effective portfolio strategies for investors. To this end, the cross-quantilogram method is employed using regional green bond and clean energy indices, together with sukuk indices of varying credit ratings, over the period from July 7, 2016, to June 6, 2024. The results of the analysis reveal a positive dependence between green bond indices and clean energy indices in the U.S. and European markets, particularly at the extreme quantiles. This finding suggests that, under certain conditions, opportunities for portfolio diversification in these markets may be relatively limited. In contrast, the negative dependence observed in the Asian market, especially at specific quantile combinations, indicates that, depending on market conditions, this region may offer potential advantages in terms of diversification and risk management. Regarding the relationship between sukuk and clean energy indices across different regions, a generally positive dependence structure is identified. Overall, the findings demonstrate that the interdependence among sustainable financial markets is both region-specific and state-dependent rather than uniform across markets. These results underscore the importance of accounting for conditional dependence structures when constructing sustainable investment portfolios and suggest that investors may achieve more effective portfolio allocation and risk management by incorporating regional market characteristics and varying market conditions into their investment decisions.
This study investigates the dynamic interplay between energy and metals prices in response to central bank interest rate adjustments. While existing literature acknowledges the heterogeneous impact of monetary policy across financial assets, the analysis of direct versus indirect transmission channels within this specific commodity-financial nexus remains underexplored. Employing a partial correlation methodology, we use daily closing prices spanning 2019 to 2025 to analyze the behavior of fourteen distinct asset classes under both calm and turbulent market conditions. Our main finding indicates that interest rate expectations and equity markets serve as significant conduits for shock propagation across other markets, particularly pronounced during periods of economic downturn. This evidence emphasizes the necessity of risk management frameworks to explicitly incorporate the influence of monetary policy and market sentiment on asset network structures, a consideration of increasing salience amidst the global transition towards green energy economies. Our findings holds even when an alternative monetary proxy is used and different time periods are considered
This study examines cojump dynamics through network modeling, analyzing both positive and negative cojumps using 5-min high-frequency data from 194 stocks within the CIS 300 index. We apply eigenvector centrality to identify key stocks within these networks and utilize community detection method to classify clusters of stocks exhibiting similar cojump patterns. The findings indicate that negative cojump network demonstrates stronger inter-stock linkages, identifies the most influential stocks in finance and real estate sectors, and exhibits greater community intensity compared to the positive cojump network. Portfolios constructed using negative cojump rankings achieve higher Sharpe ratios than those based on positive cojump networks, and the integrating of negative cojump community information further improves return predictions. Overall, these insights underscore the vital role of negative cojump dynamics in optimizing investment strategies and strengthening risk management.
This research examines the relationship between credit spread risk in the banking book (CSRBB) and bond market behavior across various maturities in twelve European nations. We address the challenges banks encounter in modeling CSRBB in isolation, as directed by the European Banking Authority’s (EBA) revised guidelines, which mandate the distinct management of CSRBB and interest rate risk in the banking book (IRRBB). Utilizing IRF and VAR models, the analysis demonstrates that credit spread shocks have divergent effects on sovereign bond I-Spreads and 5-year Credit Default Swap spreads across different tenors and countries. The results also indicate that shock absorption and transmission vary considerably between short-term and long-term bonds. Tenor sensitivity influences the structuring of risk-based capital, the development of hedging strategies, and the optimization of debt maturity. The findings question the validity of parallel shift assumptions in conventional CSRBB stress testing, indicating that term structure responses exhibit non-linearity, country dependence, and regime sensitivity. The study highlights the importance of translating IRF outputs into base, moderate, and severe shock scenarios for effective CSRBB modeling and compliance. This process allows financial institutions to improve risk mitigation strategies, ensure balance sheet resilience, and align with the EBA’s revised guidelines.
This paper examines whether implied volatility and textual news jointly improve volatility forecasting. We propose the NEWS IV model, which combines the realized-variance components of the HAR model with option-implied variance and news topics extracted via Latent Dirichlet Allocation within a flexible machine learning framework. Using data for the Ibovespa ETF and major Brazilian stocks, we evaluate predictive performance relative to HAR-type benchmarks across multiple horizons. We show that the model augmented with implied volatility and news delivers performance comparable to standard models at the daily horizon and improves forecasts at weekly and monthly horizons. The results reveal a clear horizon-dependent pattern. Implied volatility plays a central role in short-term predictions, while news-based variables become increasingly relevant at longer horizons. These findings highlight the complementary informational content of market expectations and textual data for understanding volatility dynamics.
This study investigates how extreme climate conditions and natural disasters (EC&ND) affect market and systemic risk across major S&P 500 sectors. We extend the conditional autoregressive expected shortfall (CARES) model and develop a semiparametric conditional expected shortfall (CoES) framework that directly incorporates EC&ND variables into sectoral tail-risk and systemic-risk estimation, overcoming limitations of VaR- and CoVaR-based approaches. Our methodology also introduces two novel metrics, the disaster market risk ratio (DMRR) and the disaster systemic risk ratio (DSRR), which quantify the marginal contribution of disaster-related factors to sector losses and systemic transmission. Results show that EC&ND variables significantly amplify market and systemic risk, particularly in the energy, insurance, agriculture, and real estate sectors. Extreme heat and disaster frequency are key drivers of tail losses and systemic spillovers. Backtesting confirms that extended ES and CoES models substantially improve forecasting accuracy. By revealing how climate-related shocks reshape risk interdependencies, the study offers actionable insights for financial regulators and macroprudential authorities seeking to integrate environmental risks into stress testing, capital requirements, and systemic risk surveillance.
