Evolving interdependencies across institutions and markets drive systemic financial risk, yet most forecasting models either treat assets independently or rely on static correlation structures. This limitation becomes particularly salient as cryptocurrency markets increasingly interact with traditional banking systems amid financial stress. Ignoring time-varying cross-market network structure risks understating tail risk precisely during periods when accurate systemic risk assessment is most critical. This study proposes a dynamic graph neural network (GNN) framework for systemic risk forecasting that models time-varying financial networks spanning banking institutions and major cryptocurrency assets. Nodes represent financial entities, while edges are constructed using rolling-window dependency measures that adapt to changing market conditions. Node dynamics are modeled through temporal neural architectures, and stress regimes are explicitly identified to evaluate performance under market turmoil. The empirical design includes strong temporal baselines, static-graph ablations, and cross-market removal experiments to isolate the contribution of network dynamics and crypto-market integration. Results indicate that a strong LSTM baseline achieves superior volatility forecasting accuracy in both overall and stress-period evaluations, demonstrating the competitiveness of purely temporal models. However, within the class of graph-based models, dynamic GNNs substantially outperform static-graph variants during stress periods, demonstrating the importance of time-varying network structure for capturing volatility amplification. Bank-only and full-system dynamic GNNs exhibit comparable stress-period performance, suggesting that cryptocurrency assets contribute limited incremental information to bank-specific forecasts, while remaining informative for system-level stress characterization. The findings suggest that dynamic graph representations enhance stress sensitivity and structural interpretability relative to static network models, even when they do not surpass strong temporal baselines in raw predictive accuracy. The results support a restrained view of crypto–banking contagion, emphasizing its conditional relevance during periods of market stress rather than unconditional systemic dominance.
3The characteristics of the new dual-cycle development pattern and systemic financial risk based on TVP-SVAR and MS-VAR model analyses
The characteristics of the new dual-cycle development pattern and systemic financial risk based on TVP-SVAR and MS-VAR model analyses
Read more2Bankruptcy Prediction with Financial Systemic Risk
Financial systemic risk – defined as the risk of collapse of an entire financial system vis-a-vis any one individual financial institution – is making inroads into academic research in the aftermath of the late 2000s Global Financial Crisis. We shed light on this new concept by investigating the value of various systemic financial risk measures in the corporate failure predictions of listed nonfinancial firms. Our sample includes 225,813 firm-quarter observations covering 8,604 US firms from 2000 Q1 to 2016 Q4. We find that financial systemic risk is incrementally useful in forecasting corporate failure over and above the predictions of the traditional accounting-based and market-based factors. Our results are stronger when the firm in consideration has higher equity volatility relative to financial sector volatility, the smaller size relative to the market, and more debts in current liabilities. The combined evidence suggests that systemic risk is a useful supplementary source of information in capital markets.
Read more5Capital account liberalisation and systemic financial risk: evidence from 24 countries
Capital account liberalisation can give rise to uncertainty in capital flows, which may lead to an accumulation of financial risks. This study measures the systemic financial risk indices using the coefficient of variation method with data of 24 countries from 2003 to 2019 and the impact of capital account liberalisation on systemic financial risks using the panel threshold model. Evidence shows that systemic financial risk indices vary heterogeneously across countries. The systemic financial risk indices of high-income countries are lower than those of middle- and low-income countries. Second, capital account liberalisation has an asymmetric effect on systemic financial risk with a double threshold. Low-intensity and high-intensity capital account liberalisation increases systemic financial risk. However, medium-intensity capital account liberalisation is effective in reducing systemic financial risk. Third, the heterogeneity results suggest that capital account liberalisation is conducive to reducing the financial risk of high- and middle-income countries and has the opposite effect on low-income countries. Therefore, this study recommends that countries adjust the intensity of capital account liberalisation according to their national conditions. It is necessary to establish a regulatory system for cross-border capital flows and maximise the benefits of liberalisation while safeguarding financial market stability.
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18Early Warning of Systemic Financial Risk of Local Government Implicit Debt Based on BP Neural Network Model
In recent years, local governments have boosted their local economies by raising large amounts of debt. Even though the state further strictly controls local government debt, the hidden debt formed by the local government borrowing in disguised form can infect systemic financial risks, creating an urgent need to carry out risk warning based on local government hidden debt. The paper uses the macro indicators of local government implicit debt risk at the prefecture-level city level, and introduces the micro indicators of PPP projects, financing platform bank debt, and urban investment debt to establish a BP neural network model. We not only study the contagion effect of local government hidden debt on systemic financial risks, but also predict the systemic financial risks in 2019 and construct an early warning risk system based on the prefecture-level city data from 2015 to 2018. In addition, the early warning effect of local government implicit debt on systemic financial risk under different stress scenarios is investigated. The study found that the implicit debt risk of local governments, the scale of financing platform bank debt, the scale of PPP, and the scale of urban investment bonds have a significant impact on systemic financial risks. The neural network model constructed by introducing these four variables at the same time can better predict the level of systemic financial risk. The model can also accurately predict the changes in systemic financial risks under the stress test of the increase in hidden debt of different local governments, and has a good early warning effect.
Read more4Local Government Bonds, Land Finance and Systematic Financial Risk
Local government debt plays an important role in promoting economic development, but with the continuous expansion of the scale of local government debt, coupled with the opacity of financial and debt information, local debt risk may be directly transformed into financial risk. Therefore, the potential mechanism of local government debt risk inducing systemic financial risk is studied to strictly control local government debt and prevent it at present. It is of great practical significance to mitigate systemic financial risk. At present, the research on local government debt mainly focuses on the mechanism of local government debt to amplify economic fluctuations, while the potential mechanism of local government debt risk to induce systemic financial risk is relatively few. From the perspective of local government bonds and land finance, the innovation of this paper is to depict the Chinese elements such as the maturity of local government bonds, the opacity of debt and financial information, and to integrate local government debt, land finance and financial intermediaries into the framework of general equilibrium to analyze the potential mechanism of systemic financial risk induced by local debt risk. This study finds that when the economy is facing negative productivity shocks, the decline in total output will inhibit the land demand of households and enterprises, and the decline in land prices caused by the decline in demand will reduce the land-related fiscal revenue of local governments. On the one hand, the decline of land-related fiscal revenue will reduce government expenditure; on the other hand, it will increase the pressure on local governments to repay debt and the risk of debt, which will lead to the rise of bond interest rates and the decline of bond prices. Lower bond prices will affect the balance sheet of financial intermediaries, resulting in increased leverage and risk premium of financial intermediaries. Under the balance sheet constraints, financial intermediaries will shrink corporate credit, resulting in an increase in systemic financial risk. Rising interest rates and tightening credit will restrain enterprises’ investment and land demand, further reduce total output and land prices, increase the debt risk of local governments, and form a financial accelerator mechanism in the economy, which will lead to the mutual reinforcement of the debt risk and financial risk of local governments and form a vicious circle. In addition, when financial risk rises, the default risk of local government debt is lower. Based on the trade-off between risk and return, financial intermediaries will hold more local government bonds. Credit squeeze brought by asset allocation changes will further strengthen the financial accelerator mechanism and strengthen the interaction between debt risk and financial risk. Meanwhile, although extending the bond maturity can reduce the pressure of local governments to repay their debt in the short term, the longer the bond maturity is, the greater the financial shock and economic volatility caused by local government debt risk are. The paper indicates that: for resolving local government debt risk and reducing the probability of systemic risk, we firstly need to improve the local government bond market, diversify local government bond investors, and change the current situation dominated by commercial banks; secondly, we should strengthen budget constraints and control the scale of local government debt; thirdly, we should rationally design the maturity structure of local government bonds, strengthen the transparency of local government finance and debt information, and play the role of market constraints; finally, when financial risk rises, the central bank can adopt non-traditional monetary policy to intervene in the market, which can reduce financial risk and social welfare losses.
Read more85TodyNet: Temporal dynamic graph neural network for multivariate time series classification
TodyNet: Temporal dynamic graph neural network for multivariate time series classification
Modeling Systemic Communication Risks in Global Financial Media: A Review of Networked Information Flows Across Stock, Forex, and Cryptocurrency Markets
Systemic risk in global financial markets is increasingly shaped by the dynamics of information dissemination and media communication across interconnected platforms. With the rise of digital media, social networks, and algorithm-driven information environments, financial communication has become faster, more fragmented, and more complex, amplifying the potential for cross-market contagion. This review examines existing research on systemic risk from a communication-centered perspective, focusing on how information flows, media narratives, and networked communication structures influence risk transmission across stock, foreign exchange, and cryptocurrency markets. Drawing on studies from communication research, computational social science, and financial media analysis, the paper synthesizes theoretical frameworks and methodological approaches, including network analysis, sentiment analysis, and artificial intelligence–based models. The review highlights key patterns in information diffusion, media amplification, and audience interaction that contribute to market volatility and systemic vulnerability. By integrating insights from communication studies with computational methods, this review identifies current research gaps and proposes future directions for understanding systemic risk as a mediated and socially constructed phenomenon in global financial communication.
Read more14The impact of MTSA on financial risk and volatility of marine firms
This paper documents research analysing the effects of the Maritime Transportation Security Act (MTSA) on a set of marine operator stocks listed on Nasdaq and the New York Stock Exchange (NYSE). The paper investigates whether MTSA resulted in a structural change in systematic risk for these companies. The paper conducted event studies using the market model to estimate the effect of MTSA on systematic risk. The empirical evidence shows that MTSA resulted in a structural reduction in systematic financial risk. These results had positive implications for the cost of these operators in raising capital. The results also show a substantial reduction in idiosyncratic risk and conditional systematic risk following MTSA which had a positive effect on market risk and liquidity. As a whole, MTSA was able to mitigate some of the increased financial risk associated with 9/11 for marine operators. From a policy standpoint, MTSA provided ancillary economic benefits by reducing these risks.
Read more329The multi-layer network nature of systemic risk and its implications for the costs of financial crises
The multi-layer network nature of systemic risk and its implications for the costs of financial crises
13Does financial leverage volatility induce systemic financial risk? Empirical insight based on the Chinese fintech sector
Financial leverage volatility is a significant factor contributing to the formation of systemic financial risk, which is more apparent in China's fast‐growing fintech (financial technology) field. Using the Conditional Value‐at‐risk approach (ΔCoVaR) risk metric, the generalized autoregressive conditional heteroskedasticity (Structural Vector Autoregression with Stochastic Volatility model [SV‐TVP‐SAVR]) model, and the generalized forecast error variance decomposition (Tvpdy) model, this paper discusses how financial leverage volatility shocks fintech sectoral risks and the evolution of the risk within fintech under the shocks on the basis of the classification criteria of the Chinese fintech enterprise database and daily trading data of A‐share listed companies. The statistical results show that financial leverage volatility causes risk changes across fintech sectors, which is especially significant during economic downturns or government interventions. Also, under the shock of financial leverage volatility, the fintech sectors will absorb or diffuse risks outward through spillover channels, with significant differences in the risk spillover conditions of different types of sectors. Finally, the fintech sector can produce a contagion system with the Internet consumer finance, payment, and Internet microcommercial credit sectors as the core of risks, resulting in a systemic risk crisis. Our findings have major implications for Chinese regulators to balance financial leverage and prevent systemic risks in the fintech sector.
Read moreSystem Dynamics Model for Systematic Evaluation of China’s Financial Risk
Finance is becoming more important in the national economy. Maintaining financial stability is critical not only for the financial industry’s prosperity and development, but also for a country’s political, economic, and social development. This paper will look at the mechanisms that cause systemic risk to develop and evolve, as well as how to measure systemic financial risk in multiple dimensions. To begin, create a system for evaluating financial risk in a systematic manner. Second, using the AHP and CRITIC methods to determine various indicators and market weights, create a systemic financial risk evaluation model based on the system dynamics model, and calculate the system from 2010 to 2019 comprehensive financial risk index. Finally, simulation research is conducted using the system dynamics model of systemic financial risk, and the simulation results are analyzed. The findings show that China’s financial risk has been gradually increasing since 2016, with relatively small fluctuations in risk state.
Read moreTensor graphical Lasso for cryptocurrency portfolio analytics
Recent failures of major cryptocurrency exchanges and widespread crypto scams highlight the urgent need for stronger regulatory oversight. Meanwhile, tensor-based frameworks are increasingly used to model complex interdependencies across crypto-trading platforms. A key challenge is reliably estimating precision matrices for tensor-valued processes, which can uncover hidden connections and risk propagation between exchanges, including undisclosed trading relationships and cross-platform exposures and systemic financial risks overall. However, most existing tensor precision matrix estimations assume independence, failing to account for the temporal dependencies that characterize crypto-markets. This limitation proved particularly damaging in events such as the FTX collapse, where interconnected trading activities and hidden leverage generated rapid systemic risk across platforms. To address this gap, we propose WeDTLasso, a novel graphical model for estimating precision matrices of temporally dependent tensor-valued data, able to capture how risks propagate through crypto-exchange networks over time. We derive theoretical guarantees in the form of non-asymptotic near-oracle error bounds and demonstrate WeDTLasso's value for regulatory oversight by analysing cross-platform risk transmission in cryptocurrency markets. Our results show that accounting for temporal dependencies is essential for identifying systemic risks and market manipulation and can deliver up to 50% improvements in Sharpe ratios for portfolio construction compared to existing methods.
Read moreREAL ESTATE MARKET, BANKING LENDING AND SYSTEMIC FINANCIAL RISKS: A NEW NONLINEAR DYNAMIC MODEL
Systemic risks have been the focus of economic research for several decades because they provoke dangerous consequences that cannot be mitigated with traditional macroprudential policy tools. Recently, the ESRB warned about high European banks' exposures to the real estate sector and the need to improve macroprudential regulation. The main purpose of the research paper is to develop the theoretical basis and modelling tools to clarify the nonlinear relationships between the real estate market and bank lending, thereby improving macroprudential regulation to mitigate systemic risks. We used the complex nonlinear systems methodology, mathematical theory of differential equations, the Lotka-Volterra approach for modelling nonlinear dynamics of interrelated systems, and the theory of collateral accelerator mechanism in the real estate market and banking lending. The scientific novelty of the paper is in the new theoretical and applied results: we developed a new non-linear dynamic model that sheds more light on the collateral accelerator mechanism and drivers of amplifying systemic financial risk; new insights about tipping points and structural shifts in the real estate market and banking system under specific conditions that we determined. We have found that under the collateral accelerator actions, the real estate market cannot achieve equilibrium. The real estate prices increase according to the relations we obtained, fueled by new loans. A price bubble is a dissipative structure in Prigogine's terminology, and the LTV parameter is the key driver of the rate of price increase. After reaching a threshold, the price bubble bursts, and crisis processes intensify. The paper's results are significant for deepening the understanding of the nature of systemic financial risks and assessing the drivers of crises in the banking sector and the real estate market. It can help improve banking regulation and macroprudential policy to prevent systemic crises and strengthen banks' strategic management.
Read more4Concentrated Commonalities and Systemic Risk in China’s Banking System: A Contagion Network Approach
Concentrated Commonalities and Systemic Risk in China’s Banking System: A Contagion Network Approach
1Impact of fintech on systemic risk in banking: literature review
The finance industry faces disruption when banks, as traditional incumbents, have to deal with growing competition from FinTech and BigTech companies. FinTech institutions are connected to traditional financial institutions like banks via various channels, constituting themselves as a part of the financial market and systemic risk with the possibility of spillover and risk contagion effects. The goal is to provide a literature review to detect theoretical perspectives, topics, and common issues in the connection between FinTech and systemic risk. A systematic approach to literature review was chosen, resulting in the analysis of 20 articles from the Web of Science database. Results confirm the positive connection between financial technology and systemic risk and call for future research.
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