Investigating the stability of and fluctuations in the energy market has long been of interest to researchers and financial market participants. This study aimed to analyze the Chinese energy market, focusing on its volatility and response to financial tensions. For this purpose, data from eight major financial companies, which were selected based on their market share in Shanghai’s and Shenzhen’s financial markets, were collected from January 2014 to December 2023. In this study, stock prices and trading volumes were used as the key variables to build bootstrap-based minimum spanning trees (BMSTs) using ratio mutual information (RMI). Then, using the sliding window procedure, the major network characteristics were derived to create an anomaly-detection tool using the multivariate exponentially weighted moving average (MEWMA), along with the Brent crude oil price index as a benchmark and a global oil price indicator. This framework’s stability was evaluated through stress testing with five scenarios designed for this purpose. The results demonstrate that during periods of high oil price volatility, such as during the turbulence in the stock market in 2015 and the COVID-19 pandemic in 2020, the network topologies became more centralized, which shows that the market’s instability increased. This framework successfully identifies anomalies and proves to be a valuable tool for market players and policymakers in evaluating companies that are active in the energy sector and predicting possible instabilities, which could be useful in monitoring financial markets and improving decision-making processes in the energy sector. In addition, the integration of other macroeconomic factors into this field could strengthen the identification of anomalies and be considered a field for possible research.

This paper develops a dynamic information-theoretic network framework to quantify systemic risk in China’s maritime–commodity nexus with a focus on the Yangtze River Basin using eight monthly indicators, CCFI, CBCFI, BDI, YRCFI, GAUP, MPCT, CPUS, and ASMC. We resample, impute, standardize, and difference series to achieve stationary time series. Nonlinear interdependencies are estimated via KSG mutual information (MI) within sliding windows; networks are filtered using the Planar Maximally Filtered Graph (PMFG) with bootstrap edge validation (95th percentile) and benchmarked against the MST. Average MI indicates moderate yet heterogeneous dependence (about 0.13–0.17), revealing a container/port core (CCFI–YRCFI–MPCT), a bulk/energy spine (BDI–CPUS), and commodity bridges via GAUP. Dynamic PMFG metrics show a generally resilient but episodically vulnerable structure: density and compactness decline in turbulence. Stress tests demonstrate high redundancy to diffuse link failures (connectivity largely intact until ∼70–80% edge removal) but pronounced sensitivity of diffusion capacity to targeted multi-node outages. Early-warning indicators based on entropy rate and percolation threshold Z-scores flag recurring windows of elevated fragility; change point detection evaluation of both metrics isolates clustered regime shifts (2015–2016, 2018–2019, 2021–2022, and late 2023–2024). A Systemic Importance Index (SII) combining average centrality and removal impact ranks MPCT and CCFI as most critical, followed by BDI, with GAUP/CPUS mid-peripheral and ASMC peripheral. The findings imply that safeguarding port throughput and stabilizing container freight conditions deliver the greatest resilience gains, while monitoring bulk/energy linkages is essential when macro shocks synchronize across markets.

This study examines systemic risk within the Iranian economic ecosystem by analyzing the interactions between different sectors from January 2013 to October 2022. A new systemic risk stress indicator is developed based on five key market indicators: money, equity, financial intermediaries, foreign exchange (FX), and oil markets, which are constructed using various sub-indices. Granger causality networks are used to explore the relationships between these indicators, and the results are validated by mutual information (MI)-based networks for sub-index selection. The results are further validated by an improved exponentially weighted moving average indicator that utilizes entropy distance. Additionally, a novel systemic risk stress indicator has been developed based on Granger causality findings. This indicator incorporates MI to capture time-dependent causal relationships and to analyze the interdependencies between various economic and financial indicators. The findings reveal significant bidirectional causality between specific indicators, with strong evidence of cross-market spillovers. Notably, oil price shocks and exchange rate fluctuations have substantial effects on economic stability. The analysis highlights key interconnections within the financial markets, including a significant causal link between the money market and the FX market, primarily driven by central bank interventions. The equity market also influences both the FX and oil markets, with capital outflows and sectoral composition playing a role in these dynamics. Additionally, the financial intermediaries market impacts market sentiment, and a causal relationship between FX and oil prices is established. These results suggest that policymakers and investors should consider these interconnected relationships when formulating strategies to mitigate risks in volatile economic environments. The study also calls for further research into the microstructure of sub-indices across different markets to better understand these complex dynamics.

This study focuses on stock market performance during the COVID-19 pandemic, aiming to research the co-integration among COVID-19 cases, investor sentiment, and the stock market. The data for the study comprised index returns, trading volume, turnover rate, and volatility from CSI300 index from January 2020 to December 2021. The paper planned to introduce methods that used the autoregressive distributed lag model, co-integration test, and error correction model to achieve the aims. The results show that there is a long-term co-integration relationship among these variables. However, when we consider the long-term association between COVID-19 and investor sentiment and individual stock market variables separately, we find no association among COVID-19 and market volatility, trading volume, or turnover rates. From the perspective of investor sentiment, there is no long-term relationship between investor sentiment and market volatility. Therefore, the model results show that there is a long-term relationship among the variables only when the data are integrated, but this relationship does not always persist when considering individual variables. As the COVID19 still continues, the study results would have the implications for the investment decision-making and risk avoidance to face the pandemic.

Financial markets often display nonlinear and turbulent dynamics during periods of stress, and crude-oil and global equity systems frequently demonstrate closely connected forms of instability. Earlier studies report multifractality, chaotic features and regime-dependent spillovers across commodities and equities, yet existing approaches rarely succeed in capturing both the intrinsic complexity of oil-market behavior and the changing structure of cross-asset dependence. This limitation reduces the ability to distinguish calm from turbulent regimes and weakens short-horizon risk assessment. The present study introduces a unified framework that quantifies and predicts systemic instability within the coupled oil–equity system. The analysis constructs a crude-oil complexity index based on multifractal fluctuation analysis, permutation and approximate entropy, and Lyapunov-based indicators of chaotic dynamics. At the same time, it develops an information-theoretic network of global equity and energy-sector returns and summarizes its instability through measures of edge turnover, spectral radius, degree entropy and strength dispersion. These components are combined to form the Coupled Multiscale Chaos Index (CMCI), a scalar state variable that distinguishes calm, transitional and chaotic market regimes. Empirical results indicate that Brent and WTI exhibit pronounced multifractality, elevated entropy and positive Lyapunov exponents, while the dependence network becomes more centralized, more clustered and more capable of shock amplification during high-CMCI states. The CMCI moves closely with realized volatility and provides significant predictive content for five-day variance across major global equity benchmarks, with performance superior to models that rely only on macro-financial controls. Out-of-sample evaluation shows that forecasts incorporating measures of complexity record substantially lower MSE and QLIKE losses. The findings indicate that systemic instability reflects the interaction between local chaotic dynamics in crude-oil markets and turbulence in the global dependence network. The CMCI offers a practical early-warning indicator that supports risk management, forecasting and macroprudential supervision.