Global value chains are increasingly exposed to geopolitical tensions, policy uncertainty, and institutional weaknesses, making supply-chain trade more fragile. Existing studies largely view supply chain finance (SCF) as a tool for easing liquidity constraints, while its role under political and institutional risk remains underexplored. This study examines how SCF-proxied by factoring activity in source countries-affects participation in supply-chain trade, measured by domestic value added embodied in gross exports. Using a gravity framework combined with a Rajan-Zingales identification strategy and a dataset of about 1.76 million country-pair-industry observations across 75 countries from 1995 to 2020, we find that SCF significantly promotes supply-chain trade, especially in industries with higher technological dependence on external liquidity. Crucially, the effect is stronger when destination countries face greater political risk, investment risk, or economic uncertainty. This pattern suggests that factoring not only eases cash-flow constraints but also enables exporters to transfer payment and country risk to financial intermediaries. Even during systemic financial crises, SCF plays a stabilizing role. Overall, the findings highlight SCF as a key mechanism for enhancing resilience in global value chains, rather than merely a source of working capital.
This study evaluated the correlation between Climate-Related Extreme Events (CREE), firm value, and stock price crash risk, examining whether CREE affects firm value and crash risk. We analysed 142 companies listed on the Tehran Stock Exchange over 11 years (2012-2022) using the Generalized Least Squares (GLS) method with fixed effects. The findings show a significant negative correlation between CREE and firm value in the year following CREE, and a significant positive relationship between CREE and stock price crash risk. This research addresses an underexplored dimension in the literature by focusing on the direct, quantifiable impacts of climate-induced natural disasters (floods, droughts, earthquakes) on stock market behaviour in a sanctions-affected, climate-vulnerable emerging economy (Iran). Unlike most prior studies emphasizing ESG performance or carbon emissions, this paper provides contextualized empirical evidence from a unique institutional setting characterized by state-led economic structures, limited access to international financial markets, and high exposure to extreme weather events. The findings offer insights for similar contexts, particularly other emerging economies facing climate vulnerability and sanctions (e.g., Russia, Venezuela), while cautioning against direct generalization to developed or institutionally dissimilar emerging markets. The geographical focus on a Middle Eastern emerging market with distinct structural characteristics (sanctions, state ownership concentration, limited climate disclosure mandates) contributes novel contextual evidence to the climate-finance literature, complementing prior research on developed Western economies. However, given Iran's unique institutional features, our findings should be interpreted as contextualized evidence rather than universally generalizable conclusions. The results suggest that CREE can decrease firm value and increase stock price crash risk, an important finding given that investors reward companies addressing environmental concerns with higher stock prices.
This paper examines how household digitalization affects financial asset allocation in the digital economy era. Using data from the 2017 and 2019 China Household Finance Survey (CHFS), we construct a household digitalization index based on digital access and usage. Employing a fixed effects model, we find that digitalization significantly increases both the share and variety of risky financial assets while reducing the share of risk-free assets in household portfolios. These results suggest that digitalization leads to greater portfolio diversification, particularly toward riskier investments. Mechanism analysis indicates that digitalization operates through improved financial literacy, relaxed liquidity constraints, and expanded income sources. Our findings provide micro-level evidence that the development of the digital economy can enhance the efficiency of household financial asset allocation.
We use the Residual Income Valuation Model to obtain fundamental values for sample stocks in six Eurozone markets. We then estimate the deviation between the fundamental values and actual stock prices. Subsequently, we examine whether these deviations can be systematically explained by business cycle trends, trends in local economic sentiment, global market-related uncertainty and developments in global energy prices. We find that market volatility, proxied by the CBOE Volatility Index (VIX), is an important factor, along with energy prices. Findings are similar when a second proxy for price deviations, based on Cochrane's (1994) methodology, is employed. Impulse Response Functions indicate that a shock increase in VIX tends to reduce the deviation between fundamental and actual stock prices in sample markets. We argue that the VIX serves as a contrarian indicator of market overconfidence, such that higher VIX levels are associated with lower investor overconfidence and, consequently, with smaller price deviations from their fundamental values. Conversely, lower VIX levels (i.e., heightened overconfidence) are associated with larger price deviations.
We empirically assess the interlinkages between sovereign risk, measured in terms of CDS spreads, and exchange rates for a sample of emerging markets. Our period of analysis includes episodes of severe stress, such as the Global Financial Crisis, the COVID-19 pandemic, and the Ukrainian War. Exploiting recent developments in local Gaussian partial correlation analysis and the associated nonlinear Granger causality tests, we are able to uncover linkages between assets across different segments of their joint distributions. Disentangling the effect of global factors, we show that the information on sovereign risk of other emerging economies is more relevant for the sovereign risk-exchange rate relationship than the state of developed markets' risk for all countries in our sample and for all segments of the assets distribution. The same considerations apply for the movements of the US dollar relative to other currencies, where changes in emerging market currencies are of particular interest. Nonlinear Granger causality tests show bi-directional causality for most countries, confirming the importance of multiple transmission channels. Taken together, our results highlight the importance of understanding the interlinkages between sovereign risk and exchange rates across their entire joint asset returns distribution, which can guide policymakers in debt and currency management, with coordinated regional responses potentially proving more effective than individual national actions. In terms of portfolio management, our documented bidirectional causality is valuable for predicting currency fluctuations based on sovereign risk, supporting hedging and investment strategies in periods of financial stress.
In the context of the latest technological revolution and industrial transformation, Artificial Intelligence (AI) provides new impetus for the development of corporate ESG practices. This study, based on data from 2630 A-share listed companies in China from 2010 to 2022, examines the impact of AI on corporate ESG performance and its mechanisms of action and explores the moderating effects of capital market financing environment. The results reveal that AI can notably enhance the ESG performance of listed companies in China. This finding is proven robust after robustness tests, including machine learning models. Based on information processing theory, the study further constructs a framework for mechanism analysis across the environmental, social, and governance dimensions. The results show that AI enhances corporate ESG performance through three channels: improving green innovation rate, enhancing price markup capabilities, and reducing agency costs. Heterogeneity analysis reveals that the enabling effects of AI on corporate ESG performance are more pronounced for technology-intensive firms, non-highly polluting firms, and firms in highly competitive industries. Further research suggests that optimization of the external capital market financing environment facilitates the empowering effect of AI on corporate ESG performance. This study provides practical insights for enterprises to effectively advance sustainable development through "AI+ ESG", for regulatory bodies to refine sustainable governance systems, and for capital markets to innovate in financial services for technology.
This study investigates how acquisition frequency influences acquirer risk and how this relationship is moderated by managerial wealth vested in the firm. Drawing on the behavioural agency model, we argue that managers use frequent acquisitions to reduce firm-specific risk exposure when their personal wealth is significantly tied to firm performance. Using a panel of U.S. firms, we employ multiple empirical strategies-including probit models, recursive bivariate probit, and semi-parametric estimation-to examine the association between acquisition frequency, managerial wealth, and two measures of acquirer risk: asset return volatility and cash flow volatility. Frequent acquirers are significantly less risky than non-frequent acquirers, and the relationship between managerial wealth and risk is inverted U-shaped among frequent acquirers, but not among infrequent acquirers. These findings suggest that executives with high levels of vested wealth initially tolerate more risk but later prioritise diversification to protect their holdings. This study offers novel insights by integrating behavioural agency theory with empirical acquisition strategy and firm risk models. It contributes theoretically by demonstrating that frequent acquisitions serve as managerial risk-management tools and empirically by identifying nonlinear effects of managerial wealth on acquisition behaviour and risk outcomes.
This paper examines the relationship between carbon performance, climate governance, and equity risk. Using a sample of companies listed in the S&P500 index for the period 2009-2023, our results show that better carbon performance reduces equity risk, indicating that proactive carbon management reduces uncertainty and is beneficial to firms. Likewise, companies that embrace climate governance practices benefit from lower equity risk, thus providing incentives towards incorporating climate change issues at board level. While both factors individually contribute to lower equity risk, specifically total and unsystematic risk, their combined benefit is less than the sum of the individual effects, suggesting that firms may benefit from focusing on one factor when the other is already well developed. This evidence, built on solid measures and a comprehensive analysis, provides recommendations to companies and policymakers towards enhancing carbon policies and strengthening climate governance commitment, which are rewarded by the financial markets in terms of lowering equity risk.
This paper investigates the impact of economic growth on insurance (growth), focusing on the outflow side of the insurance activity, as captured by benefits (including commissions and expenses). The findings provide evidence that economic growth does exert a positive, statistically significant impact on the benefit side of insurance (growth) for all relevant metrics (claims, commissions and expenses) and all insurance branches (total, life and non-life), which constitutes the main novelty of the paper and its contribution to the extant literature. At the same time, it reaffirms that economic growth posts a positive, statistically significant effect on the acquisition side of insurance (growth) for all pertinent variables (total, life and non-life premia, total, life and non-life penetration). The results remain robust for pre-/post- and during the 2008-crisis periods, the pre-/post- and during the 2020-pandemic periods, as well as the pre- and post- Solvency II era and different county-income tranches (lower, upper, and higher). The same holds true when economic policy uncertainty and geopolitical risks are considered. All these are also fresh discoveries of the paper and additions to the existing research. Consumption and R&D are shown to work as transition mechanisms between economic growth and insurance growth, while they also receive support from panel causality tests. This constitutes the primary theoretical innovation of the study. The findings imply that policymakers need to address key economic and financial economic matters to facilitate insurance growth.
The evolutionary mechanisms of regional currency networks and the sources of core currency influence remain critical yet underexplored questions in international finance. While existing literature examines exchange rate spillovers, it rarely investigates the hierarchical structure of currency networks or the differentiated roles various currencies play. Using daily exchange rate data for 13 Asian currencies and 4 international currencies from 2000 to 2023, this paper employs a time-varying parameter factor-augmented vector autoregressive model with elastic network regularisation, combined with social network analysis, to systematically examine the structural dynamics of Asian currency networks. The findings reveal a four-stage evolution of expansion, disruption, reorganisation, and re-disruption, which closely aligned with major economic and geopolitical events. The Renminbi functions as an "influential core" primarily through trade scale and economic size, whereas the Singapore dollar serves as a structural "bridge currency" during financial crises. Panel regressions confirm that bilateral trade intensity and capital account openness are key drivers of Renminbi spillovers. This study provides the first empirical distinction between two types of currency core status, offering new evidence for understanding regional currency networks and Renminbi internationalisation.
As rural digital infrastructure improves, digital literacy is emerging as a critical form of human capital for sharing the dividends of the digital economy, profoundly influencing the financial behaviour of rural households. Based on data from the China Rural Revitalization Survey (CRRS), this paper examines the impact of the enhancement of rural households' digital literacy on their household debt. The core finding is that enhanced digital literacy significantly boosts the probability and the magnitude of rural household debt. We subsequently identify two important mechanisms. Firstly, higher digital literacy is associated with a significant decrease in subjective income satisfaction, which in turn motivates households to debt to bridge perceived psychological disparity. Secondly, our findings indicate that enhanced digital literacy incentivizes households to increase their debt to capitalize on e-commerce opportunities. Further heterogeneity analyses reveal that the stimulating effect of digital literacy on rural household debt is stronger among economically advantaged rural households, in villages with developed logistics, and in non-suburban rural regions.
This article examines the risk properties of freight-derivative-based exchange-traded funds (ETFs), focusing on the Breakwave Dry Bulk Shipping ETF (BDRY), and evaluates the accuracy of Value-at-Risk (VaR) and Expected Shortfall (ES) forecasts across a range of econometric models. Motivated by the growing financialisation of shipping markets and the emergence of ETFs as accessible freight-risk instruments, the study addresses a gap in the literature, which has largely focused on physical freight rates and derivatives rather than securitised exposures. Using daily data from 2020 to 2025, we implement Historical Simulation, GARCH-type models with alternative distributions, Extreme Value Theory, and forecast combination methods, and assess performance using regulatory backtesting frameworks. The results show that models incorporating time-varying volatility and heavy-tailed distributions outperform simpler specifications, while forecast combination approaches consistently deliver strong performance across VaR and ES measures. ES forecasting remains particularly challenging, especially at extreme confidence levels, highlighting significant model risk. Evidence from stylised investment strategies confirms the economic value of accurate tail-risk measurement. The findings have important implications for portfolio management and financial regulation, supporting the use of model averaging and robust backtesting in the context of derivative-based ETFs and Basel III/IV risk frameworks.
In this paper, we test whether institutional diversity in banking systems is beneficial to economic complexity, using data for Italian provinces in the period 1998-2017. We compute different indexes that consider diversity from an ownership, institutional, business model and competition point of view and find that higher diversity has a positive role on economic complexity. Results come from system-GMM estimation, are not influenced by the financial cycle and are robust to changes in the lag structure, dependent variables, specification and spatial dependence. They point to the beneficial role of banking diversity for innovation and growth of local ecosystems.
We investigate whether bond mutual fund managers exhibit market liquidity timing skills in the U.S. corporate bond market. At the portfolio level, we find only weak evidence that bond funds adjust their overall market exposure in anticipation of changes in corporate bond market liquidity. In contrast, when liquidity timing is examined through sector allocation strategies across investment grade, high-yield, and mortgage-backed securities (MBS) sectors, we find strong evidence of high-yield sector liquidity timing ability-fund managers overweight high-yield bonds as corporate bond market liquidity improves. Using individual fund level analysis, we find that top-ranked bond funds demonstrate market liquidity timing skills with respect to both the overall market and all three sectors. Bootstrap analyses indicate that these liquidity timing skills of bond fund managers are unlikely to be driven by luck. Moreover, we find evidence of persistence in sector liquidity timing ability over time, especially for high-yield sector timing. Finally, these sector liquidity timing strategies help predict future fund performance.
Amid intensifying global economic uncertainty, commodity markets have become a sensitive indicator of economic vitality. This study first employs the LASSO-VAR-DY model to build a spillover matrix and then uses a dynamic quantile network econometric model to analyze the impact of VIX shocks on global commodity markets. Results show that commodities display significant volatility and inter-category differences. The VIX index triggers pronounced asymmetric spillover effects, more so in extreme markets. Global commodity prices are spatially linked, with stronger spillover effects during price increases or violent market fluctuations. The impact of commodities' own lags also exhibits significant quantile heterogeneity. These findings enhance macro-level understanding of systemic risk transmission in commodity markets, help reduce market uncertainty and maintain financial stability, and offer theoretical support for investors and policymakers.
This article examines the governance of Latin American central banks, focusing on trade-offs arising from multiple mandates, governance design, and central bank board characteristics. The analysis combines a qualitative review of central bank statutes with an original panel dataset covering eight Latin American central banks over the period 2004-2023. Using fixed-effects panel regressions, the study explores how governance arrangements and board composition are associated with inflation outcomes, the inflation target level, and the inflation target interval. The results indicate that governance design is primarily associated with differences in the structure of inflation-targeting frameworks rather than with inflation levels. Higher levels of central bank independence are consistently associated with narrower inflation target intervals, which may reflect tighter control over short-run inflation deviations. Board member characteristics display selective associations: political affiliation and board heterogeneity are linked to inflation outcomes in some specifications, while academic qualifications, gender composition, and professional background are more closely related to the calibration of inflation-targeting instruments, particularly the inflation target interval. These associations are stronger for continuous measures of policy design than for binary indicators of inflation target achievement, which are more closely related to macroeconomic conditions and inflation persistence. Overall, the findings suggest that central bank governance in Latin America operates mainly through institutional design and policy calibration rather than through immediate adjustments in inflation outcomes.
Based on the dynamic capability theory, we examine how climate risks (CR) affect supply chain resilience (SCR) using data from China's A-share-listed companies (2013-2023). The results show that CR significantly enhances SCR. Additionally, CR enhances SCR primarily through three paths: expanding supply chain financing, enhancing supply chain efficiency, and fostering supply chain collaborative innovation. Meanwhile, executive monetary compensation incentives and external market attention both amplify the positive effect of CR on SCR, while equity incentives show no significant moderating role. Heterogeneity analysis reveals that CR exerts a greater influence on SCR among firms with superior ESG performance, greater supply chain transparency, larger trade credit financing, and lower industry competition. Sub-item testing indicates that the positive impact primarily stems from transition risks. Collectively, this study extends the research frontier at the intersection of CR and SCR and provides actionable guidance for firms navigating climate challenges.
Extensive research finds that Payments for Watershed Services (PWS) yield positive ecological and economic effects. However, prior studies have yet to investigate whether Gamble-based Payments for Watershed Services (GPWS) generate similar positive outcomes. Using a sample of Chinese A-share listed firms located in provinces that signed GPWS agreements during 2009-2023, this paper employs a staggered DID approach to examine the effect of GPWS on corporate green innovation. We document asymmetric upstream-downstream effects: GPWS promotes green innovation among upstream firms while impeding such innovation in downstream firms. This asymmetry arises because GPWS strengthens environmental regulations and increases environmental subsidies in upstream regions, while weakening environmental regulations and reducing environmental subsidies in downstream regions. Moreover, GPWS exerts a stronger inhibitory effect on green innovation in downstream firms than its promotive effect in upstream firms.
This paper integrates time-inconsistent preferences and ESG investment into a dynamic q-theory framework. We demonstrate that time-inconsistent preferences induce systematic under-investment, with the magnitude of inefficiency critically dependent on a firm's reputation. Productivity volatility () systematically reshapes corporate strategies: rising reduces investment but increases ESG expenditure. Reputation mediates a strategic trade-off: low-reputation firms suffer investment crowding-out from ESG spending, while high-reputation firms leverage ESG as a buffer against volatility-induced distortions. These findings unify the reputation repair hypothesis and buffer effect under a dual-regime strategy driven by reputation heterogeneity.
Amid growing global environmental challenges, supply chains have become a critical conduit for the transmission of environmental pressures. However, existing research presents theoretical divergences regarding the effectiveness of customer environmental pressure transmission, primarily due to the neglect of customers' subjective cognitive appraisal of such pressures. Grounded in the cognitive appraisal theory of stress, this study constructs an environmental pressure perception index by analysing the tone of annual report texts, aiming to elucidate the impact and underlying mechanism of customer environmental pressure perception on suppliers' greenwashing. Using a sample of Chinese A-share listed companies from 2009 to 2022, we find that customer environmental pressure perception significantly curbs the extent of supplier greenwashing. Mechanism analysis reveals that customer environmental pressure perception inhibits greenwashing through two pathways: improving the quality of suppliers' environmental information disclosure and increasing their environmental investments, with these two pathways exhibiting a chain-mediating effect. Heterogeneity analysis indicates that this inhibitory effect is more pronounced when customers exhibit high information disclosure quality, when suppliers are private enterprises, or when supply chain dependency is strong. By emphasising the pivotal role of subjective perception rather than objective pressure, this study not only offers a cognitive appraisal perspective to explain theoretical disputes concerning pressure transmission within supply chains, but also provides important implications for enterprises and policymakers in effectively mitigating greenwashing and enhancing the overall green performance of supply chains.
