This paper examines the transmission of US risk-free rates to interest rates in decentralized finance (DeFi) lending market for USD-pegged stablecoins. Although DeFi protocols operate without central banks, their connection to the traditional financial system remains an open question. We use a theoretical model of DeFi lending that incorporates arbitrage between traditional and crypto markets. The model predicts that interest rate transmission depends on DeFi-specific factors, including utilization rates, crypto market conditions and the persistence of lending rates. To test these predictions, we construct a novel dataset from Ethereum blockchain data covering USDT, USDC and DAI. Our empirical results show that risk-free rate has statistically significant predictive content for stablecoin lending rates. Higher risk-free rates are correlated with higher deposit rates, and this correlation operates through the utilization channel. However, this effect is largely mediated by crypto-native factors. These findings have important implications for financial stability monitoring and for understanding the transmission of interest rates through novel digital asset markets.
This study examines the moderating role of CEO-duality in the relationship between board diversity and firm performance, using panel data from non-financial listed firms in China from 2010 to 2024. The findings reveal a significant positive impact of board diversity on firm performance. Furthermore, CEO-duality positively moderates the diversity-performance relationship, aligning with the stewardship theory perspective. The results suggest that CEO-duality, when combined with a diversified board, strengthens board decision-making, and enhances firm performance. These effects are more pronounced in non-state-owned enterprises (non-SOEs) compared to state-owned enterprises (SOEs). Our findings are robust using the corporate governance index with 27 board characteristics. The study is also confirmed using the system GMM approach and different board attributes in robust tests underscoring the important role of CEO-duality and board diversity and in improving firm performance, particularly in emerging market contexts.
This study explores the dynamic interactions between price shocks to commodity markets and investor demand, proxied by fund flows, in environmental, social, and governance (ESG)-labeled global equity and bond funds. By focusing on labeled funds, the analysis isolates the sustainability signal that investors observe at the point of allocation. Using a time-varying parameter vector autoregression connectedness framework and controlling for broad equity and bond market conditions, the paper shows that ESG-labeled fund flows are less integrated with the broader system than conventional benchmarks on average, but they display clear asymmetries across commodity classes. Energy commodities are the main commodity transmitters to ESG equity fund flows, whereas industrial metals are the main commodity transmitters to ESG bond fund flows. Dynamic spillovers intensify sharply around the COVID-19 market dislocation and the Russia-Ukraine shock. In the second-stage analysis, stock-market uncertainty dominates in the full sample and pre-crisis period, whereas interest-rate uncertainty dominates during COVID-19 and oil-market uncertainty dominates during the Russia-Ukraine period. These findings show that investor demand for labeled sustainability exposure is state dependent and responds differently to transition-related shocks across equity and bond ESG segments.
This study examines whether macroeconomic volatility affects corporate leverage decisions and whether subnational institutional quality moderates this relationship. Using an unbalanced panel of 500 non-financial Vietnamese listed firms over 2010–2024 (6,423 firm-year observations), we estimate dynamic partial-adjustment leverage models in which macroeconomic volatility affects both the direction of leverage adjustment and the speed at which firms close the gap to their model-implied target. Institutional quality is proxied by Vietnam’s Provincial Competitiveness Index, interpreted as subnational institutional buffering capacity. System GMM estimates indicate that, evaluated using quintile-specific coefficients, a one-standard-deviation increase in macroeconomic volatility is associated with an approximately 4.8-percentage-point reduction in leverage among firms in the lowest institutional-quality quintile, compared with an imprecisely estimated reduction of approximately 1.4
Binding margin constraints can affect asset prices and trading activity, yet their im- pact in retail-dominated emerging markets remains insufficiently understood. This paper examines stock-level margin eligibility removals (“margin cuts”) on the Ho Chi Minh Stock Exchange during 2020–2025. Using a matched difference-in-differences design that pairs margin-cut stocks with ob- servationally similar margin-eligible peers, the analysis isolates the effect of security-level funding shocks on prices and trading activity. The results show that margin cut announcements are followed by a sharp and persistent decline in stock prices, with treated stocks underperforming matched con- trols by approximately 5
This study investigates the impact of corporate hedging as a tool of financial risk management on firm cost of public debt in U.S. public non-financial firms from 2001 to 2021. Using a panel data of 2,500 public non-financial firms and 4,859 bond-year observations in the U.S from 2001 to 2021, we find that corporate hedging during 2001–2021 leads to a reduction of 29.98 bps in corporate bond yield spreads. Interestingly, it causes an increase of 38.3 bps in corporate cost of debt during the crisis period from 2008 to 2010. The results are consistent across different credit ratings. There is evidence on the effect of macroeconomic conditions on the link between corporate hedging and firm cost of debt. This paper attempts to fill the gap in literature on the effect of corporate hedging and the components of corporate bond yield spreads as well as providing additional empirical assessment on how corporate hedging affects firm cost of debt financing during and after the financial crisis.
This manuscript investigates the nonlinear impact of Geopolitical Risk (GPR) on the returns of five leading cryptocurrencies, including Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), Binance Coin (BNB), and Tron (TRX), spanning the period from 2017 to 2025. By employing the novel Quantile-on-Quantile Granger Causality (QQGC) approach and the Quantile-on-Quantile Regression (QQR) for robustness, the analysis explores how geopolitical shocks drive market dynamics across various quantiles. The empirical results reveal significant asymmetric transmissions and distributional heterogeneity, indicating that the influence of GPR varies across different market conditions and specific digital assets. These findings demonstrate that geopolitical instability acts as a critical driver of price movements in the cryptocurrency ecosystem. Consequently, the results provide essential insights for investors and policymakers regarding risk mitigation and strategic portfolio allocation. This research contributes to financial literature by clarifying the complex interplay between non-financial global shocks and digital asset pricing in an increasingly uncertain international environment.
This paper examines how geopolitical risk affects firm valuation and whether environmental, social, and governance (ESG) performance moderates this relationship in Asian markets. Using a panel of non-financial listed firms from Asian economies over 2000–2024, we find that geopolitical risk is negatively associated with firm value. We further show that this adverse association is weaker among firms with stronger ESG performance, indicating greater valuation resilience under geopolitical stress. Pillar-level evidence suggests that the moderating role is more pronounced for the Social and Governance dimensions than for the Environmental dimension. Additional analyses also show that the documented patterns vary across industries, financial-development environments, ownership structures, and firm size. Overall, the findings suggest that ESG is associated with greater resilience in firm valuation when geopolitical tensions rise in Asian markets.
In this paper, we analyze the moderating role of government effectiveness in the relationship between carbon emissions and financial development across 90 countries over the period 1996–2016. Using the Method of Moments Quantile Regression (MM-QR) as the primary method, complemented by dynamic and cross-sectional dependence-robust panel estimates, we find that government effectiveness can counteract the adverse environmental effects of financial development. This moderating effect is not linear and weakens monotonically across higher levels of the conditional emissions distribution. We also examine differences across income groups and find that the interaction between financial development and government effectiveness is strongest in lower- and middle-income countries, while it becomes statistically insignificant in high-income countries. The findings offer policy implications for aligning financial development with environmental sustainability through stronger institutional capacity.
This paper investigates the dynamic relationship between investor attention to generative artificial intelligence, proxied by Google search trends for ChatGPT, and U.S. equity market dynamics. Utilizing a structural vector autoregression framework with weekly data from December 2022 to February 2026, we analyze the conditional responses of equity returns and trading volumes across four major indices. We document a persistent predictive relationship, where positive shocks to information demand correspond to increased valuations for technology-oriented equities and declining valuations for traditional industrial sectors, indicative of a capital rotation effect. While the incorporation of aggregate stock volumes reveals a consistent decline in macro-level trading activity, supporting an immediate consensus repricing mechanism, supplementary firm-level estimations demonstrate significant volume spikes alongside targeted price movements. This confirms that the observed capital rotation is driven by highly specific, active capital reallocation toward direct AI integrators and away from perceived competitors and legacy physical capital, rather than merely reflecting index concentration. Extending the empirical framework to state-dependent specifications demonstrates that these stock market dynamics are highly conditional on the prevailing macroeconomic environment, including shifts in interest rates, economic activity, oil prices, and exchange rates. Finally, restrictive structural orderings reveal significant weakening in these predictive paths, confirming that information demand and asset pricing are simultaneously determined as interconnected dynamic relationships rather than strict, unidirectional causal effects.
This study answers the question of whether rare earth metals behave as segmented strategic commodities driven by sector‑specific fundamentals or as financially integrated transition assets by estimating time‑varying connectedness among rare earths, clean energy, information technology, and precious metals using a TVP‑VAR framework. Research uncovers hedging and diversification opportunities among these assets by estimating hedge ratios and optimal portfolio weights. The results reveal a relatively low average level of connectedness across the assets, with pronounced spikes during periods of heightened uncertainty, notably the COVID‑19 pandemic and the Russia-Ukraine conflict. The low connectedness is economically consistent with baseline segmentation in normal periods; however, during crises, the study found episodic integration and stronger spillover transmission. Clean energy consistently emerges as a net transmitter of shocks, while rare earth and precious metals act primarily as net receivers. Spillovers are predominantly concentrated in the short run, indicating that market interactions intensify during a crisis but weaken over longer horizons. The hedging analysis suggests that precious metals offer the most cost-effective hedge for rare earth exposures, whereas clean energy provides limited hedging efficiency. Optimal portfolio weights suggest that rare earth metals should comprise a relatively smaller proportion of diversified portfolios due to their higher volatility and supply concentration risks. The study provides evidence-based insights for investors and policymakers seeking to understand better volatility transmission across key sectors supporting the global energy transition.
This study examines the influence of family ownership and ownership concentration on the risk of financial distress among Indian non-financial companies. It contributes to the Indian literature on financial distress by examining how family ownership structure and concentration shape distress risk, which has been given limited attention in existing studies. It employs secondary data on Indian non-financial firms collected from the CMIE Prowess database for 2001 to 2023. Using pooled logit models to predict the risk of financial distress, the study finds a significant negative association between family ownership and the risk of financial distress. High ownership concentration reduced the risk of financial distress, whereas multiple promoters in family firms increased it. Financial indicators, such as improved liquidity, efficiency, and better cash flow and leverage management, also lowered distress risk across firm samples. The findings suggest that family ownership with fewer promoters could enhance the financial resilience of the firm and highlight the need for strong financial management practices, particularly in liquidity management and cash flow optimisation, to mitigate distress risk. The findings also underscore the governance role of family ownership and suggest that concentrated and coordinated family control can strengthen firms’ financial resilience.
The global financial markets are driven by internal and external factors, among which market sentiments remain critical, especially at the height of global crises and uncertainty. Taking insight from the varying crises, including the metal market crash and the stock crashes, this study examines the conditional connectedness of risk within the industrial stock markets and the part of market sentiments (fear, market news, financial stress). This study employed the nine (9) univariate GARCH models in estimating the best market risk while also using the quantile vector autoregression (QVAR) in modelling the conditional connectedness using daily data from February 2014 to April 2025. Among the best risk models highlighted were the threshold GARCH, exponential GARCH, nonlinear GARCH, asymmetric power GARCH, and the component standard GARCH. The QVAR estimations justified the conditional and time-varying risk connectedness among industrial stocks and market sentiments. The median quantile was largely characterized by persistence in both risk and sentiments, while the upper quantile exhibited contagion with excessive transmission in both the static and time-varying estimations. The stocks transmitted significant risk towards FMS and news, confirming that news-based sentiment and financial stress are not generated extensively, but through investor reactions to market pricing dynamics. Most industrial stocks (HON, CTAS, GRD, TRT, and ITW) were net transmitters of risk in both the static and time-varying results, with all the sentiment measures assuming the role of receivers, except for financial market stress at the upper quantile, validated in the net pairwise directional connectedness. The study confirms the excessive responsiveness of investors to varying sentiments through heightened connectedness at extreme quantiles. In general, this research upholds a more proactive, multidimensional, and emotion-sensitive approach to risk regulation that reflects the intricate nature of contemporary financial markets.
This paper explores the impact of digital transformation on loan interest rates by incorporating digital transformation into a multi-sectoral interest rate decision model for banks from a "cost-income" perspective. We find that digital transformation reduces loan interest rates through the channels of reducing banks' administrative costs and increasing income quality. Further research reveals that the loan interest rates of non-state-owned banks and those located in the central and western regions are more significantly affected by digital transformation. The study also indicates that the impact of digital transformation on bank interest rates mainly comes from the digital transformation of business. Our study enriches the research on the relationship between digital transformation and bank loan interest rates.
This study examines the relationship between currency devaluation and the volatility of Bitcoin returns and mining profitability from 2015 to 2025 in major mining hubs characterized by significant exchange-rate exposure and cost sensitivity. While prior literature has focused on demand-side volatility drivers, this research places greater attention on supply-side fluctuations through the exchange rates channel. Applying DCC-GARCH and Regime-Switching Copula methods, we demonstrate that depreciation of the local currency reduced mining costs, increased the Bitcoin return volatility, and its responsiveness to macro-financial linkages. These results underline the importance of USD hedging and dynamic taxation regimes for Bitcoin miners. The study progresses the modeling of volatility through incorporating the exchange rate elasticity into the analysis of market interdependence in the crypto-asset sector.
Predicting stock price movements remains challenging due to the noisy, nonlinear, and multimodal nature of financial markets. While existing studies combine technical indicators with news sentiment, they often lack transparency and fail to uncover temporal predictive relationships across modalities. This paper proposes a predictively explainable multimodal forecasting framework that integrates structured price-based features and unstructured financial news using FinBERT embeddings within a recurrent neural network. A dual explainability scheme is employed: Integrated Gradients for feature attribution and Neural Granger causality to reveal temporally ordered predictive relationships. Using 17 years of Nifty 50 data (2003–2019) aligned with 73,500 financial news headlines, the model is evaluated under a strict rolling-origin time-series cross-validation protocol to prevent data leakage. Results demonstrate superior predictive accuracy, significantly outperforming a comprehensive set of statistical, machine-learning, and financial benchmark models, with significance validated via the White Reality Check. An ablation study confirms news embeddings as the dominant predictive modality. Granger-based analysis further reveals that specific semantic dimensions systematically predict future returns and volatility. A transaction-cost-aware trading simulation confirms the model’s economic relevance, yielding economically meaningful risk-adjusted performance. The study presents a scalable, interpretable, and robust framework for financial forecasting, offering insights for both academic research and real-world decision-support systems.
This study examines extreme tail-risk spillovers among energy-intensive cryptocurrencies (Bitcoin, Bitcoin Cash, Ethereum, Ethereum Classic, Litecoin, Monero), three major green assets (S P Green Bond Index, S P Global Clean Energy Index, S P ESG Leader Index), and two key commodities (Gold, WTI Crude Oil) across quantiles and frequency horizons. Using a quantile–frequency connectedness framework, we evaluate spillover patterns during bullish, bearish, and stable market conditions, with emphasis on the COVID-19 pandemic and Russia–Ukraine conflict. Results show that cryptocurrencies—particularly Bitcoin and Ethereum—are dominant short-term net transmitters during periods of elevated volatility. Conversely, green assets and commodities, especially Gold, consistently act as net receivers, reflecting stability during market stress. Spillovers weaken and relationships normalize under tranquil conditions. While Gold and WTI absorb short-term shocks, they become balanced over longer horizons. Overall, cryptocurrencies exert short-run influence, whereas traditional and green assets demonstrate long-term resilience, offering important implications for portfolio diversification and risk management.
This Study investigates the empirical question of whether opening up to international trade and finance alters the demand for money in developing economies. Using annual panel data of 24 developing countries from 1990 to 2022, along with disaggregated KOF Globalization Index, we examine whether the economic aspects of globalization have a different monetary impact than the composite index, which also takes social and political aspects into account. Three estimators are used to handle cross-sectional dependence, serial correlation, and heteroskedasticity: fixed effects with clustered robust standard errors, Driscoll–Kraay, and panel-corrected standard errors. The Durbin–Wu–Hausman test and a lagged specification are used to formally address reverse causality. The results are consistent across all estimators and both globalization measures. The increased openness considerably increases the demand for real money balances. The economic sub-index yields larger elasticities than the aggregate index, indicating that economic integration as the principal pathways through which openness alters monetary behavior. With near-unity elasticities, real income is the most powerful determinant, whereas inflation has a negative impact that is consistent with the inflation tax channel. Using the Bai-perron structural break analysis, two regime shifts are located; 1997 and 2007-08 corresponding to Asian financial crisis and Global financial crisis respectively. The sub-period estimation reveal that the positive openness effect is statistically non-existent before 2008 and materializes afterwards as developing economies deepen economic integration into global markets. Omitting openness in money demand models will produce systematic forecasting errors in an increasingly integrated economy.
This study investigates the non-linear relationship between environmental, social, and governance (ESG) performance (proxied by LSEG ESG data) and short-term market reactions to the announcement of seasoned equity offerings (SEOs) among listed manufacturing firms in the U.S. The event-based design mitigates concerns of reverse causality, which often arise in studies analyzing the relationship between ESG performance and company value. We document evidence of an inverted U-shaped relationship for total ESG and social pillar scores from 2020 to 2023. Environmental pillar scores are negatively associated with market reactions over the same period. The results are insignificant over the period 2016 to 2019. Buy-and-hold abnormal returns (BHARs) and underpricing are not found to be related to ESG performance. The observed non-linear pattern is consistent with interpretations in which moderate ESG engagement is viewed more favorably by investors, whereas very high levels of ESG engagement may be associated with concerns about costs or symbolic activity, inter alia.
Patents have been shown largely in country-level analyses to be associated with higher levels of real GDP. This current endeavor focuses on the U.S. county level and tests whether having more patents is associated with higher real GDP, while controlling for other important factors such as labor (employed persons), median income and demographic factors such as racial composition. and educational attainment. Using a 13-year panel data set (2010–2022) for U.S. counties, the authors find a consistent, positive “patents effect” on real GDP, with a slightly stronger positive relationship for three- and five-year lagged patent counts, compared to ten-year lags, suggesting that the effect on real GDP may diminish with time. A similarly-positive effect on real GDP is identified using the total stock of patents variable, measured as a twenty-year running sum. This analysis has important policy implications, such as providing support for tax incentives or subsidies to attract patent-producing companies as a means of stimulating regional economic growth.
