Search Results (425)

Grain price stability is fundamental to global food security, yet the extent to which major grain futures markets contribute to international price discovery and transmit price signals across borders remains insufficiently understood. This study constructs a measurement–mechanism–pathway framework to analyze the international price transmission dynamics of four grain futures: wheat, rice, corn, and soybean. We apply the Diebold–Yilmaz spillover index, OLS regressions with mediation analysis, and fsQCA. All four crops display predominantly negative net spillover indices, with rice exhibiting the strongest transmission and soybean the weakest. At the domestic level, yield is associated with stronger price transmission through an inventory channel, while the effects of price regulation are asymmetric. Notably, soybean producer subsidies are unexpectedly associated with stronger rather than weaker international price transmission. At the international level, crude oil prices are associated with weaker grain price transmission primarily through the freight cost channel, and speculative capital is associated with reduced transmission capacity through tail-price episodes. Configuration analysis reveals two shared improvement routes, namely capacity building with policy support and risk containment via volatility suppression, together with four commodity-specific pathways. These findings provide systematic evidence and policy insights for mitigating grain price contagion and strengthening food system resilience. Full article

China’s “dual carbon” targets and stricter green finance regulations have increased compliance pressures on manufacturing firms. In response, some firms engage in greenwashing—exaggerating their environmental performance or concealing negative information. If greenwashing spreads through interlocking directorate networks, it poses a threat to green financial stability. However, existing research primarily focuses on individual firm motivations, leaving the mechanisms of network contagion and their boundary conditions insufficiently understood. Using panel data on A-share manufacturing firms from 2009 to 2023, we employ two-way fixed-effects models to test for peer greenwashing contagion and examine how financing constraints moderate this effect. Our findings reveal significant contagion within manufacturing interlocking directorate networks: firms facing fewer financing constraints are more sensitive to peer greenwashing. This effect is more pronounced in highly marketized regions, in high-tech industries, and among firms with advanced digital transformation. Full article

The present study investigates extreme co-movements and financial contagion across a broad set of global financial markets, including ten developed and emerging stock market indices, commodities (gold and oil), and cryptocurrencies (Bitcoin), over the period from January 2007 to May 2023. In the context of the increasing interconnectedness of global financial markets, it is imperative to comprehend the propagation of systemic shocks across asset classes for the purpose of effective risk management. In order to achieve this objective, a Copula-GARCH framework is employed, in which the Student’s t-copula is selected for its superior ability to capture nonlinear dependence and tail co-movements. The analysis compares dependence structures during the pre-crisis and the COVID-19 crisis periods. The present study diverges from the majority of previous research in its utilisation of a combined approach, integrating Copula-GARCH modelling with wavelet analysis. This novel method is employed to collectively examine tail dependence and multi-scale contagion dynamics, thereby facilitating a more comprehensive evaluation of financial interconnectedness during periods of market stress. The empirical evidence indicates significant and largely symmetric tail dependence across the majority of market pairs. This finding suggests the presence of stronger co-movements during periods of extreme market conditions, a phenomenon that was particularly evident throughout the course of the global pandemic. The robustness of these findings is further confirmed by wavelet analysis, which provides a multi-scale perspective on shock transmission across markets. The results demonstrate that financial contagion intensified during the pandemic, with important implications for international portfolio diversification and risk management. Furthermore, the role of gold as a potential safe-haven asset during periods of severe financial stress is highlighted, providing valuable insights for investors and policymakers. Full article

Although stablecoins occupy a segment of digital-asset markets in which price stability is central by design, their temporary departures from reference values may reveal important information about latent risk and market stress. In this paper, we examine whether bubble and crash signals extracted from traditional cryptocurrencies and stablecoins improve volatility, Value-at-Risk, and Expected Shortfall forecasting and, in connection with these forecasting gains, contribute to the assessment of cross-asset contagions. The analysis applies the Bubble Crash–GARCH models, in which extreme price phases are identified through the Phillips, Shi, and Yu real-time monitoring procedure and incorporated into the conditional mean of returns through event-based dummy variables. For stablecoins, extreme episodes are not inferred from price dynamics in isolation but from deviations between the observed price and the asset-specific reference value. The empirical investigation focuses on Bitcoin, Ethereum, Tether’s USD-pegged (USDT), and Tether Gold and evaluates asset-specific bubble–crash effects and bidirectional contagion channels between traditional cryptocurrencies and stablecoins, using Bitcoin and Tether as the leading representatives of the two market segments. The findings indicate that accounting for bubble and crash episodes leads to more accurate volatility forecasts than standard GARCH benchmarks. For Value-at-Risk and Expected Shortfall, the bubble–crash specifications can improve tail risk forecasting at several tail probability levels through more accurate coverage, lower quantile loss, and stronger ESR backtesting performance. The results also reveal different degrees of price exuberance across the two asset categories: while extreme price dynamics are more evident among traditional cryptocurrencies, deviations from fundamentals are rare for stablecoins. Among stablecoins, USDT exhibits limited but detectable exuberance, whereas Tether Gold does not display extreme price episodes. However, when such deviations occur, as in the case of USDT, they generate significant contagion effects on major cryptocurrencies. Notably, extreme episodes originating in USDT have a stronger impacts on Bitcoin and Ethereum than the reverse spillovers from traditional cryptocurrencies to USDT. Overall, the evidence suggests that stablecoins are not merely passive instruments within the digital-asset ecosystem. Even temporary deviations from their reference values contain valuable information for risk forecasting and contagion monitoring. Full article

Financial contagion—the propagation of shocks across markets, sectors, and time—remains one of the central questions in empirical finance, and yet most computational approaches model it at the wrong granularity. Existing work captures contagion at the asset level by measuring realised return or volatility comovements between specific securities, which conflates a structural pattern with its surface manifestation. We argue that the genuine causal regularity in contagion is between event types (rate decisions, default announcements, regulatory actions) rather than between individual assets. This paper introduces TECG, a framework that learns time-stamped causal-temporal association edges—predictive dependencies rather than identified causal effects—between abstract financial event types from news text and uses them to forecast multi-step contagion cascades. Methodologically, TECG can be read as a neural impulse response function (IRF) for event sequences: where the canonical structural-VAR IRF traces the dynamic response of one continuous variable to a shock in another, TECG traces the conditional intensity with which one event type fires after a shock to another event type, at a learned and possibly multi-modal lag. The framework couples LLM-based event extraction with a multivariate neural Hawkes process whose intensity functions are parameterised by a temporal graph neural network. The resulting graph has interpretable, time-stamped edges of the form “event-type A triggers event-type B with lag distribution L and conditional intensity κ.” We evaluated TECG on an aggregated corpus of approximately 482,000 financial news items spanning 2007–2023 and report three findings. First, the learned edges recover associations consistent with known causal relationships in finance—central-bank announcements preceding sector-level earnings revisions, default events at one institution preceding due-diligence events at competitors—without supervision on these relationships. Second, edges learned from data up to 2018 transfer to held-out cascades from 2020 (COVID equity crash) and 2023 (US regional banking stress) with substantially better fidelity than baselines that ignore temporal structure or operate at the asset level. Third, the framework supports generalised impulse response analysis: given a hypothetical seed event, it produces a distribution over downstream event chains that an analyst can interrogate, time-stamp, and entity-resolve. We are explicit that the empirical validation is observational and that the causal interpretation rests on assumptions we discuss at length. All headline comparisons are supported by paired-bootstrap significance tests; the extraction front end is independently evaluated on a manually annotated benchmark, with an explicit error-propagation analysis; and additional comparisons against recent temporal point-process and temporal-graph baselines are reported. The framework’s principal limitation is that it does not separately identify endogenous fire-sale or leverage-spiral dynamics; we point to where these gaps could be closed. Full article

The increasing systemic importance of the insurance sector implies that risks originating within it can propagate across industries through complex interconnections, potentially exerting significant impacts on China’s broader economy. This study investigates the transmission and spillover mechanisms of intra-industry risk among insurance-related institutions from a network perspective. Specifically, a DCC-GARCH-CoVaR framework is employed to quantify risk spillovers among six listed financial institutions engaged in insurance-related businesses. Based on forecast error variance decomposition, an insurance network is constructed and further examined using network topology analysis to characterize its structural properties. The findings indicate that, despite gradual improvements in resilience and stability across institutions, network structural characteristics remain critical drivers of the formation and diffusion of systemic risk. Highly connected and central institutions are more likely to act as contagion hubs, facilitating rapid outward risk transmission, particularly in densely connected networks with shorter path lengths. Moreover, a nonlinear relationship exists between network structure and systemic risk, with leverage growth playing a moderating role. Elevated leverage amplifies the risk-enhancing effect of network centrality, while under certain conditions, mitigating adverse effects associated with passive risk exposure. Additionally, capital adequacy conditions shape risk dynamics: well-capitalized institutions can utilize network connections for risk diversification, whereas undercapitalized institutions tend to intensify contagion. Significant heterogeneity is also observed in the effects of coreness, density, and path length across institutional groups. Full article

Within a coordinated fiscal financial policy framework, this study combines complex network analysis with cellular automata to construct a contagion model of debt risk across industrial enterprises. It then uses numerical simulations to examine the dynamic evolution and mitigation strategies of debt risk contagion. The results show that the following: (1) As the contagion probability, immunity failure probability, and contagion probability of immune enterprises increase, debt risk contagion among industrial enterprises is strengthened, whereas higher immunity probability and recovery probability improve network stability. (2) Market noise, carbon tax rate, credit interest rate, and risk preference increase the basic reproduction number relative to the critical boundary of one, whereas fiscal subsidy intensity, green credit ratio, and risk assessment capability reduce it. Within the normalized simulation framework, a carbon tax rate around 0.3, fiscal subsidy intensity around 0.15, and green credit ratio around 0.5 serve as illustrative model-based reference values for interpreting changes in debt risk contagion pressure and risk-mitigation effects. (3) Coordinated fiscal–financial intervention can more effectively reduce $R_0$ and narrow the contagion scope than a single policy tool, suggesting that debt risk prevention should combine fiscal support, green credit allocation, risk assessment improvement, and carbon-policy rhythm management. Full article

Rapid urbanization reshapes landscape structure and threatens ecosystem services, yet operational ecological benchmarks for environmental impact assessment remain limited. Focusing on China’s Guanzhong region, this study examines how landscape fragmentation influences the ecosystem service value (ESV) and identifies critical threshold points for planning and management, using land-use data from 1990 to 2020 combined with spatial and statistical analyses. The results show that urban expansion substantially increased landscape fragmentation, with patch density rising by 109%, and contagion declining by 83.7%. Although the total ESV showed partial recovery over the study period, the landscape pattern mediated approximately 62% of the total effect of land-use change on ESV, constituting the dominant transmission pathway. Three actionable thresholds were identified: patch density of 1.2 patches·km⁻², the largest patch index of 18%, and contagion of 15%. Beyond these breakpoints, ESV loss accelerates sharply, with the most pronounced rate increase reaching 355.6%. These findings provide region-specific reference values to support environmental impact assessment, ecological zoning, and land-use regulation in rapidly urbanizing areas. Full article

We introduce the Zealot–Preacher model, a generalized diffusion framework on networks with simultaneous heterogeneity at the node and edge levels. Standard diffusion models typically assume homogeneous susceptibility across nodes and homogeneous transmission strength across edges. We relax both assumptions by assigning to each node a latent receptiveness parameter and to each edge a latent conductiveness parameter governing influence propagation. The framework accommodates both binary contagion and continuous consensus dynamics and nests standard homogeneous diffusion as a special case. We characterize the resulting weighted consensus process and show that low-receptiveness nodes disproportionately influence long-run network outcomes. To estimate the model in large networks, we develop a hierarchical latent-variable approach in which receptiveness and conductiveness follow Beta distributions, with hyperparameters estimated by maximum likelihood and individual effects recovered through Bayesian updating. An application to U.S. interlocking director networks shows that diffusion of board gender diversity is constrained primarily by widespread node-level resistance rather than by the absence of conductive transmission channels. Female shared directors exhibit higher average conductiveness than male shared directors, suggesting gendered heterogeneity in diffusion channels. Full article

As the global economy accelerates toward low-carbon transformation, climate financial risks are emerging as a key challenge to monetary policy design and financial stability oversight. This study examines the contagion effects and dynamic interdependencies among domestic climate-sensitive industries, financial climate risk indices, and international climate markets. Using daily data from April 2020 to April 2025, we apply a multiscale tail risk modeling framework that integrates wavelet decomposition, conditional volatility modeling, and vine-copula techniques to capture time-varying and asymmetric dependence structures across markets. The results show that the three markets display volatility clustering, fat tails, and nonlinear dependence. The international climate market shows weaker and more volatile connections with the two domestic markets, suggesting that external climate expectations operate mainly through indirect dependence across market states. The risk spillover results further show that climate financial risk contagion differs between upside and downside states and varies across short and medium horizons. These findings have important implications for integrating climate risk into macroprudential surveillance. Central banks and regulators should strengthen early warning mechanisms, climate stress testing, and scenario analysis by considering market-specific, nonlinear, and multiscale risk spillovers. The main contribution of this study is to integrate multiscale decomposition, conditional volatility modelling, and vine-copula dependence analysis into a unified empirical framework for identifying climate financial risk contagion across markets. The findings offer useful evidence for climate stress testing, early warning systems, and financial stability monitoring in emerging markets. Full article

In the post-truth era, effective governance of emergency public sentiment faces significant challenges due to the phenomenon of opinion deviation. Although online public opinion has been extensively investigated, the specific impact of Public Opinion Deviation (POD) on the evolution of group negative emotion remains inadequately understood. To address this gap, this study proposes an explicable framework that integrates community detection, text mining, and opinion dynamics. Opinion deviation communities are identified by applying the Louvain algorithm and TextRank to social media data, followed by a deviation analysis of community topics against core issues. Subsequently, a multi-stage quantification model is constructed to measure the severity of POD. During these processes, we develop a novel Opinion Dynamics F-J model (POD F-J model) and its intervention-oriented model (IPOD F-J model), which incorporate the quantified POD severity to simulate the evolution of group negative emotion. Our findings demonstrate an intrinsic correlation between the severity of opinion deviation and the intensity of group negative emotion at the information level, thereby confirming the necessity of targeted intervention. Simulation experiments indicate that different intervention strategies should be adopted depending on the situation. Moreover, the application of a greedy algorithm identifies the time points corresponding to the peak severity of deviation and its onset as the efficiency-oriented intervention timings. This study provides a data-driven framework for monitoring and mitigating emotional contagion in deviation communities, contributing to both the theory and practice of digital governance. Full article

Digital transformation (DT) reshapes supply chain ecosystems and promotes inter-firm synergistic development. Using a sample of 2417 focal firm–partner dyads of Chinese A-share listed firms from 2013 to 2023, we employ regressions with industry and year fixed effects and mediation analysis to examine how focal firms’ DT affects partners’ internal control (IC) quality. We find that focal firms’ DT enhances partners’ IC quality, robust to various tests (e.g., IV, PSM). Mechanism analysis reveals two distinct pathways: transformation contagion (focal firms’ DT drives partners’ synchronized DT) and management spillover (focal firms’ DT-driven control activities exported to partners). Heterogeneity analysis shows that the positive transmission effect is stronger in geographically distant or low-concentration supply chain relationships, as well as for focal firms with greater market power. This study extends research on IC determinants beyond firm boundaries and shifts DT externality research from operational to governance outcomes, providing a governance-level synergistic pathway to supply chain sustainability. Full article

Façades play a decisive role in shaping the visual and symbolic character of historic urban environments. Recent European funding schemes promoting energy-efficient retrofitting have accelerated interventions on building envelopes. Although aligned with decarbonization objectives, these processes are generating significant chromatic and material transformations that risk eroding the visual coherence and cultural sustainability of consolidated urban areas. In the historic Ensanches of San Sebastián, the replacement of traditional envelope systems with new cladding solutions is leading to the loss of the architectural style of some facades and altering their materials, textures, and colors. A progressive “contagion effect” has been identified, whereby dissonant chromatic schemes—often associated with the proliferation of so-called “zebra blocks”, residential buildings with façades clad in alternating black and white stripes that have proliferated in recent urban developments—are replicated across adjacent buildings, gradually weakening spatial continuity and the genius loci of the neighborhood. In response to this phenomenon, this research develops a systematic methodology to analyze, quantify, and anticipate chromatic transformation in consolidated urban fabrics. The study combines historical morphological analysis, classification of architectural periods, and chromatic mapping of recent façade interventions. Based on this framework, a CARI, Chromatic Alteration Risk Index is proposed to evaluate the potential impact of façade alterations on urban chromatic coherence. Drawing on an epidemiological framework, the methodology enables the identification of critical transformation clusters, the assessment of contagion dynamics, and the definition of regulatory thresholds for color and material interventions. By integrating perceptual criteria, urban morphology, and spatial distribution patterns, the study moves beyond descriptive diagnosis and offers a transferable tool for municipal planning. The proposed approach supports the proactive regulation of façade rehabilitation processes, balancing energy efficiency objectives with the preservation of collective memory, material identity, and urban sensory quality. This study proposes a quantitative model of “urban chromatic contagion” to assess how façade color interventions propagate within a neighborhood. We define the Chromatic Integration Percentage (CIP) and the Chromatic Alteration Risk Index (CARI) of the analyzed area. Results indicate that poorly regulated façades show higher chromatic dissonance (low CIP) and act as contagion hotspots, while a clear risk gradient emerges: highly protected buildings present lower risk, whereas mixed typologies and recent rehabilitations concentrate higher CARI values. The model supports preventive urban color management by identifying areas at risk before visible alteration. Full article

To systematically examine the impact of climate risks on China’s financial system, this study employs the EGARCH-SGED model to precisely fit financial market volatility based on China’s Climate Change News Index. It then combines the LASSO-CoVaR method to measure tail risk spillover effects within China’s financial system under climate risk shocks, constructs a risk contagion network, and innovatively utilizes the RF-AdaBoost model to establish the risk early warning system. Findings reveal that climate risk is a key driver of dynamic correlation evolution within the financial system, with heterogeneous impacts across different markets. Physical climate risk events intensify short-term risk contagion while generating long-term effects; transition risks undergo a dynamic process, initially amplifying uncertainty before enhancing systemic stability over the long term. The RF-AdaBoost model outperforms traditional machine learning models in risk warning, demonstrating outstanding predictive accuracy and generalization capabilities, thereby providing effective intellectual support for climate risk prevention and financial stability management. Full article

Against the backdrop of intensifying global climate change, climate policy uncertainty (CPU) and investor sentiment have become critical factors influencing the stability of financial markets. In this study, a quantitative index of investor sentiment is constructed using stock trading volume, turnover rate, price-to-earnings ratio, circulating market value, and the consumer confidence index. The QVAR-DY model is employed to analyze the risk contagion mechanisms among CPU, investor sentiment, and China’s financial sub-markets across different quantiles. Furthermore, five machine learning models—LSTM, BiLSTM, CNN, XGBoost, and LightGBM—are used to forecast risk spillover indices, and their performance is compared with three benchmark models (ARIMA, Persistence, and HistMean) to systematically evaluate the advantages of machine learning models in capturing tail risk spillover effects. The findings reveal significant cross-market risk contagion in financial markets, characterized by asymmetry. The level of risk spillover under extreme conditions is substantially higher than under normal conditions, indicating high sensitivity to extreme events and major policies. CPU exhibits the most pronounced spillover effect on the money market, while investor sentiment has the greatest impact on the stock market. The stock, real estate, and commodity markets act simultaneously as sources of risk and receivers of shocks. In terms of forecasting performance, LightGBM performs best under normal conditions, whereas LSTM achieves the highest prediction accuracy under extreme conditions. Full article