Abstract This study investigates the volatility spillover and connectedness networks among renewable energy sources (Biofuel, Fuel cell, Geothermal, Solar), green bonds, and cryptocurrencies (Bitcoin, Ethereum, Tether, and BNB coin) in the U.S. market. To accomplish this objective, we analyzed data from November 15, 2017, to May 31, 2024, via the methods introduced by Diebold and Yilmaz (Int J Forecast 28:57–66, 2012) and Baruník and Křehlík (J Financ Econometr 16:271 296, 2018). Our findings reveal that major global disruptions—including the COVID-19 pandemic, the Russia–Ukraine war, the collapse of Silicon Valley Bank, and the Credit Suisse crisis—have intensified volatility spillovers and financial contagion across markets, exacerbating their outcomes. The findings suggest that the effectiveness of green finance depends on its allocation across these sectors, highlighting the importance of examining each sector to understand the success of these financial initiatives. The influence of COVID-19 on the U.S. economy has increased transmission risk across markets. Renewable energy is less volatile than green bonds and cryptocurrencies are, with these indices reacting more quickly to short-term shocks. Investors should focus on short-term impacts to manage market risk effectively. By providing insights into how financial shocks propagate across sectors, emphasizing the need for a sector-specific approach to assessing financial sustainability, and underscoring the importance of short-term risk management strategies, this research offers valuable contributions to decision-makers and investors.

The interdependence inherent in interbank networks amplifies vulnerability to systemic risk, particularly through correlated asset exposures during exogenous negative shocks. This study employs exponential random graph models (ERGMs) to reconstruct a bipartite network of asset-holding correlations based on the balance sheets of Chinese commercial banks from 2016 to 2022. The reconstructed network closely approximates the topological features of the actual banking system. We then introduce a novel framework for measuring aggregate network vulnerability, which incorporates bank size, initial shocks, interconnectedness, leverage, and asset fire sales to capture key channels of financial contagion. Our results indicate that the reconstructed network aligns closely with empirical data in both link structure and weight distribution. Furthermore, cumulative systemic vulnerability increases non-linearly with the severity of the initial shock and the discount depth of fire sales. For individual banks, indirect vulnerability driven by contagion via deleveraging and fire sales significantly exceeds direct losses from initial shocks. Systemic risk contributions are concentrated in large state-owned banks and nationwide joint-stock commercial banks, whereas the institutions most susceptible to risk shocks are predominantly small and medium-sized rural and urban commercial banks.

This study investigates the evolving interdependencies and risk transmission mechanisms between energy and financial markets, focusing on gold, West Texas Intermediate (WTI) crude oil, the S&P 500 Index (SP500), and the Shanghai Stock Exchange Composite Index (SSE) from January 2019 to August 2025. Employing daily return data, the analysis integrates multivariate linear regression, Dynamic Conditional Correlation-GARCH (DCC-GARCH) models, and dynamic conditional R² decomposition to capture time-varying connectedness and explanatory power. Grounded in the Diebold and Yılmaz (2012, 2014) framework, augmented by R² decomposition, the findings evince heightened systemic risk during major global disruptions, notably the COVID-19 pandemic and the Russia Ukraine conflict. WTI crude oil emerges as a principal conduit of volatility spillovers, underscoring its pivotal role in the energy-finance nexus. Five portfolio strategies Minimum Variance, Minimum Correlation, Minimum Connectedness, Minimum R², and Minimum Decomposed R² are constructed and evaluated under systemic stress. While the Minimum Variance Portfolio delivers robust risk-adjusted returns, strategies based on connectedness metrics demonstrate superior resilience during crises. These insights offer valuable implications for policymakers, investors, and scholars concerned with energy market stability, financial contagion, and adaptive portfolio design.

Retraction notice to “Identifying the multiscale financial contagion in precious metal markets” [FINANA 63 (2019) 209–219

This study examines the relationship between systemic risk, cryptocurrency adoption, and financial performance in Nigeria, a leading African economy characterised by persistent macroeconomic instability, regulatory uncertainty, and financial informality. While cryptocurrencies and blockchain-based innovations are widely recognised for their potential to enhance financial inclusion and capital mobility, their integration into fragile monetary systems remains uneven. Drawing on comparative trend analysis covering the period 2014–2024, the study explores how decentralised crypto platforms, peer-to-peer exchanges, and stablecoin usage influence key financial indicators, including banking sector liquidity, exchange rate stability, and capital adequacy. The analysis is anchored in Financial Contagion Theory, Market Integration Theory, and Institutional Theory to explain how technological disruption, weak governance structures, and informal financial practices interact to reshape systemic risk exposure in emerging economies. The findings indicate that although cryptocurrency adoption improves transactional efficiency and access to alternative assets, its widespread use in Nigeria weakens monetary policy transmission, intensifies capital flight, and heightens financial volatility, particularly in the absence of effective regulatory oversight. The study recommends the development of a unified regulatory framework, enhanced market surveillance mechanisms, and inclusive financial governance models that align crypto innovation with Nigeria’s financial stability objectives and broader digital economy agenda.

Financial crises increasingly exhibit complex, interconnected patterns that traditional risk models fail to capture. The 2008 global financial crisis, 2020 pandemic shock, and recent banking sector stress events demonstrate how systemic risks propagate through multiple channels simultaneously—e.g., network contagion, extreme co-movements, and information cascades—creating a multidimensional phenomenon that exceeds the capabilities of conventional actuarial or econometric approaches alone. This paper addresses the fundamental challenge of modeling this multidimensional systemic risk phenomenon by proposing a mathematically formalized three-tier integration framework that achieves 19.2% accuracy improvement over traditional models through the following: (1) dynamic network-copula coupling that captures 35% more tail dependencies than static approaches, (2) semantic-temporal alignment of textual signals with network evolution, and (3) economically optimized threshold calibration reducing false positives by 35% while maintaining 85% crisis detection sensitivity. Empirical validation on historical data (2000–2023) demonstrates significant improvements over traditional models: 19.2% increase in predictive accuracy (R2 from 0.68 to 0.87), 2.7 months earlier crisis detection compared to Basel III credit-to-GDP indicators, and 35% reduction in false positive rates while maintaining 85% crisis detection sensitivity. Case studies of the 2008 crisis and 2020 market turbulence illustrate the model’s ability to identify subtle precursor signals through integrated analysis of network structure evolution and semantic changes in regulatory communications. These advances provide financial regulators and institutions with enhanced tools for macroprudential supervision and countercyclical capital buffer calibration, strengthening financial system resilience against multifaceted systemic risks.

Abstract How can we detect and quantify multi-dimensional financial contagion in emerging markets before crises manifest? Systemic risk assessment in emerging financial markets requires capturing complex contagion mechanisms that extend beyond traditional pairwise relationships, yet existing frameworks fail to model the higher-order dependencies that characterize crisis propagation. This study develops an integrated framework combining Transfer Entropy networks, hypergraph-based risk analysis, and deep learning stress testing to quantify multi-dimensional systemic vulnerabilities in Turkish financial markets from 2015-2025. The methodology introduces three novel metrics: Risk Virality Score using PageRank on directional information flow networks, Hyperedge Anomaly Detection for early warning capabilities, and Financial Immunity Score synthesizing contagion resistance, recovery speed, and structural risk exposure. Empirical analysis of ten sectoral indices and seven macroeconomic variables reveals severe vulnerability in the banking sector contrasting with exceptional resilience in eight other sectors, identifies 25,319 higher-order risk relationships invisible to pairwise analysis, and demonstrates early warning capabilities with 2-4 week lead times before major stress episodes. The BiLSTM-Attention stress testing reveals that domestic political uncertainty generates more persistent impacts than external shocks across most sectors. These findings challenge conventional risk assessment approaches and support enhanced macroprudential frameworks incorporating network effects and higher-order relationships for emerging market financial stability.

As a cornerstone of the modern financial safety net, the Lender of Last Resort (LOLR) is essential in mitigating liquidity crises and containing financial contagion. However, during periods of economic stability, risk-taking incentives in the banking sector may undermine its effectiveness. Using quarterly panel data from 55 countries over the period 2010–2023, this study employs a two-way fixed effects model to assess the impact of LOLR support on systemic financial risk and its transmission mechanisms. We find that LOLR support significantly increases systemic risk during stable economic periods. Mechanism analysis indicates that this effect is channeled through the erosion of bank asset liquidity, expansion of financial leverage, and deterioration in asset quality. Moreover, the adverse impact is more pronounced in emerging economies, bank-dominated financial systems, countries with low capital adequacy ratios, underdeveloped regulatory frameworks, and lower levels of digital technology adoption. This study provides cross-country evidence on the potential negative consequences of central bank rescue functions during calm periods and offers important policy insights for optimizing the LOLR framework and building a more resilient financial safety net.

Purpose This study investigates the dynamic and asymmetric spillover effects of cryptocurrency volatility on traditional financial markets (equities, FX, sovereign bonds) in emerging economies. It assesses the extent, evolution and country-level heterogeneity of these transmissions. Design/methodology/approach The study employ a two-stage econometric framework. First, ARIMA–GARCH models filter returns and estimate conditional volatilities. Second, a Time-Varying Parameter Vector Auto-Regression (TVP–VAR) model with Generalized Forecast Error Variance Decomposition (GFEVD) captures the magnitude, direction and temporal evolution of volatility spillovers. The dataset comprises daily data (2015–2023) for Bitcoin, Ethereum and Binance Coin and financial indicators from India, Brazil, Turkey, Indonesia and South Africa. Findings The findings reveal significant, time-varying spillovers from cryptocurrencies to traditional markets, intensifying during bear markets and crises. Cryptocurrencies act as net transmitters of volatility under stress. Spillover intensity varies across countries, with Turkey and India exhibiting the highest exposure due to restrictive policies and regulatory ambiguity, while South Africa's neutral stance results in lower connectedness. Originality/value This study empirically assess asymmetric and country-specific volatility spillovers from cryptocurrencies to traditional markets using a TVP–VAR–GFEVD framework. The findings contribute to financial contagion theory and offer crucial insights for risk monitoring and regulatory design in emerging markets shaped by digital asset dynamics.

This paper presents Long Short-Term Memory (LSTM) networks for detecting and predicting financial spillovers in the Gulf Cooperation Council (GCC) stock markets. Although widely used, traditional Vector Autoregression (VAR) models do not capture the nonlinear and asymmetric dynamics common in regional financial contagion, especially during crises. We apply LSTM networks to the daily returns of four major GCC stock indices: Saudi Arabia, Oman, Dubai, and Qatar. The sample spans 2,273 trading days from March 2012 to December 2024.

We propose a new nonparametric modeling framework for causal inference when outcomes depend on how agents are linked in a social or economic network. Such network interference describes a large literature on treatment spillovers, social interactions, social learning, information diffusion, disease and financial contagion, social capital formation, and more. Our approach works by first characterizing how an agent is linked in the network using the configuration of other agents and connections nearby as measured by path distance. The impact of a policy or treatment assignment is then learned by pooling outcome data across similarly configured agents. We demonstrate the approach by deriving finite‐sample bounds on the mean‐squared error of a k‐nearest neighbor estimator for the average treatment response as well as proposing an asymptotically valid test for the hypothesis of policy irrelevance. We illustrate the empirical applicability of our method with simulations and an application to social capital formation.

The study examined the connectedness among bitcoin, green bonds (represented by the US S&P Green Bond Index), renewable energy (represented by the OMX Biofuel Index), and gold, utilizing a novel quantile connectedness approach from 14 November 2017 to 30 May 2024. This approach contributes to understanding the transmission mechanisms, influence, and connectedness among the bitcoin, green bond, renewable energy, and gold markets. The result indicates that significant values appear at specific intervals. A significant spike was observed at specific intervals around 2019, mainly due to the trade war between the U.S. and China. A subsequent shock occurred between 2020 and 2021, driven by the COVID-19 pandemic. Moreover, the US credit crisis exacerbated volatility spillovers and financial contagion across markets, worsening these effects in 2023 and intensifying volatility spillovers and financial contagion across markets, exacerbating their outcomes. Additionally, the results suggest that Bitcoin primarily serves as a receiver of shocks. At the same time, the green bond transmits the shocks, and renewable energy and gold have switched between transmission and receiving shock roles during the period. The findings offer valuable insights into sustainable portfolio construction, highlighting that green bonds serve as primary transmitters of shocks and suggest a role as diversification anchors during market stress. Additionally, recognizing Bitcoin as a shock absorber and the shifting roles of renewable energy and gold help investors optimize risk-hedging strategies and enhance portfolio resilience across varying market conditions. This indicates that understanding how these assets correlate across various market scenarios is crucial to maximizing portfolio performance while accounting for sustainability constraints.

Using a time-frequency and quantile connectedness approach, our study examines the complex return spillovers dynamics between BRICS Plus stock markets, the volatility index (VIX), and the global geopolitical risk index (GPRD). By employing advanced models such as TVP-VAR, quantile connectedness, and spectral decomposition, we demonstrate how these markets interact across different market conditions and periods. Our results indicate that the VIX consistently acts as the dominant net transmitter of shocks, especially during periods of heightened uncertainty such as the COVID-19 pandemic, the Russian-Ukraine conflict, and the Trump-era U.S.-China trade tensions. In contrast, the GPRD functions predominantly as a net receiver of shocks, indicating its potential role as a hedge during geopolitical crises. BRICS Plus markets exhibit heterogeneous behavior: Brazil, South Africa, and Russia frequently emerge as net transmitters, while China, India, Egypt, Saudi Arabia, and the UAE primarily act as net receivers. Spillovers are strongest at the extremes of the return distribution and are mainly driven by short-term dynamics, underscoring the importance of high-frequency reactions over persistent long-term effects. These findings highlight the asymmetric, nonlinear, and state-dependent nature of global financial contagion, offering important insights for risk management, asset allocation, and macroprudential policy design in emerging market contexts.

Financial contagion within the interbank network

Mathematical modeling of epidemics is a cornerstone in the study and response to the spread of diseases and related processes across various domains. However, classical models generally do not describe such memory effects properly and are computationally inefficient, which restricts their applicability or predictive accuracy. To address these issues, we introduce a new approach to epidemic modeling using our newly proposed fractional-order differential equations, which are endowed with the Atangana–Baleanu system to describe long-range dependencies and nonlinear characteristics more accurately than the traditional Caputo system. To address this, we develop physics-informed neural networks and Fourier-based artificial intelligence-driven surrogate solvers, which are computationally efficient without compromising accuracy. To actuate intervention policies in a dynamic fashion, we also incorporate a hybrid control mechanism integrating the use of reinforcement learning with classical mathematical optimization to facilitate adaptive policymaking that benefits from data. Unlike existing work, our framework is rigorously evaluated on real-world epidemiological datasets from the World Health Organization and the Centers for Disease Control and Prevention, and tested extensively for out of- the-box adaptability to cybersecurity (cyber malware), social rumor, and financial contagion problems. We also propose a data-free generative model (Fair4Free) that improves fairness, privacy, and utility in synthetic dataset generation, allowing its use even for constrained-data settings. Experimental evidence indicates that our holistic approach enhances the accuracy of predictive performance compared to baselines, with both lower computational cost and cross-domain generalizability to unprecedented settings. Finally, we set a new state-of-the-art for EpiModel by end-to-end training on fair data.

ABSTRACT Traditional measures of financial contagion rely on correlation shifts, overlooking higher moments such as skewness and kurtosis. We examine contagion during two major financial crises, incorporating lower‐ and higher‐moment measures. We analyze stock market returns from 22 major markets at different frequencies, offering a global perspective often missing in previous studies. Employing higher‐order dependence measures, we demonstrate that conventional methods risk losing valuable information. Our contagion networks highlight how shocks travel outward. We find no systematic differences between developed and less‐developed economies’ vulnerability and stress the need for utilizing higher‐order measures when assessing financial stability to avoid underestimating contagion risks.

This study examines volatility spillovers between Chinese and U.S. equity markets by developing a comprehensive framework that captures asymmetric volatility, extreme co-movements, and dynamic correlations. We propose an integrated methodology combining EGARCH models with Student-t innovations, a Student-t copula, and a Dynamic Conditional Correlation (DCC) structure. Using daily returns of the Hang Seng Index (HSI) and the S&P 500, our analysis reveals three principal findings. First, the EGARCH model effectively captures the pronounced leverage effect and fat-tailed distributions characteristic of both markets. Second, the Student-t copula demonstrates the best fit among competing specifications, indicating significant symmetric tail dependence between the two markets. Third, time-varying correlations exhibit high persistence, rising during crises yet remaining within a moderate range. Crucially, out-of-sample forecasting shows that our unified framework achieves superior predictive accuracy relative to standard benchmarks. These findings provide valuable insights for investors designing hedging strategies, exchanges determining margin requirements, and policymakers monitoring financial contagion. Our approach offers a robust tool for analyzing volatility transmission between developed and emerging markets.

Retraction notice: Financial contagion in financial markets: a systematic literature review and directions for future research

This study analyzes the effects of financial contagion and good corporate governance (GCG) on bankruptcy prediction models in ASEAN banks (Indonesia, Singapore, Malaysia, the Philippines, and Thailand). Using secondary data from 145 banks (82 healthy, 63 bankrupt) covering 2016–2024, a logistic regression model was applied in three stages: without moderation, with contagion and GCG as moderating variables, and with them as independent variables. The results show that financial contagion increases the probability of bankruptcy, while GCG reduces it. Profitability and liquidity decrease bankruptcy risk, solvency increases it, and risk management shows no significant effect. The study highlights the dual role of contagion in amplifying risk and GCG in mitigating bankruptcy likelihood.

Understanding the interconnectedness of financial markets is essential for assessing market efficiency, risk transmission, and financial integration, particularly in the context of global shocks. While the COVID-19 pandemic has prompted numerous empirical investigations into international stock market co-movements, important gaps remain regarding the integration between developed and emerging European markets. This study explores the degree of integration between the Romanian stock market and major developed Western European markets, with a particular emphasis on Germany, Romania’s largest trading partner. Employing partial wavelet coherence analysis, we analyze the co-movement between the German DAX and Romanian BET indices across three distinct periods: before, during, and after the COVID-19 pandemic. The findings reveal that the pandemic temporarily weakened market integration, but this effect was reversed in the post-pandemic period, with integration not only recovering but intensifying. These results carry important implications for investors, policymakers, and scholars concerned with financial contagion, regional integration, and the resilience of emerging markets in the European context.