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The European Union Emissions Trading System (EU ETS) serves as the cornerstone of European climate policy, providing a critical mechanism for mitigating greenhouse gas emissions. Accurate forecasting of the carbon allowance prices within the market is essential for policymakers, enterprises, and investors. To address the need for interval-valued time series modeling and forecasting in the carbon market, this paper proposes a Transformer-based multi-task learning framework that integrates online news and search engine data information to forecast interval-valued EU carbon allowance futures prices. Empirical evaluations demonstrate that the proposed framework achieves superior predictive accuracy for short-term forecasting and remains robust under high market volatility and economic policy uncertainty compared to single-task learning benchmarks. Furthermore, ablation experiments indicate that incorporating news sentiment intensity and search index effectively enhances the framework's predictive performance. Interpretability analysis highlights the critical role of specific temporal factors, while the time-varying variable importance analysis further underscores the influence of carbon allowance close prices and key energy market variables and also recognizes the contributions of news sentiment. In summary, this study provides valuable insights for policy management, risk hedging, and portfolio decision-making related to interval-valued EU carbon prices and offers a robust forecasting tool for carbon market prediction. [ABSTRACT FROM AUTHOR]

The paper adopts the financial physics approach to investigate influence of trading volume, market trend, as well as monetary policy on characteristics of the Chinese Stock Exchange. Utilizing 1-minute high-frequency data at various time intervals, the study examines the probability distribution density, autocorrelation and multi-fractal of the Shanghai Composite Index. Our study finds that the scale of trading volume, stock market trends, and monetary policy cycles all exert significant influences on micro characteristics of Shanghai Composite Index. More specifically, under the conditions of large trading volumes, loose monetary policies, and downward stock trends, the market possesses better fitting on Levy’s distribution, the volatility self-correlation is stronger, and multifractal trait is more salient. We hope our study could provide better guidance for investment decisions, and form the basis for policy formulation aiming for a healthy growth of the financial market.

With the increasing importance of the stock market, it is of great practical significance to accurately describe the systemic risk of the stock market and conduct more accurate early warning research on it. However, the existing research on the systemic risk of the stock market lacks multi-dimensional factors, and there is still room for improvement in the forecasting model. Therefore, to further measure the systemic risk profile of the Chinese stock market, establish a risk early warning system suitable for the Chinese stock market, and improve the risk management awareness of investors and regulators. This paper proposes a combination model of EEMD-LSTM, which can describe the complex nonlinear interaction. Firstly, 35 stock market systemic risk indicators are selected from the perspectives of macroeconomic operation, market cross-contagion and the stock market itself to build a comprehensive indicator system that conforms to the reality of China. Furthermore, based on TEI@I complex system methodology, an EEMD-LSTM model is proposed. The EEMD method is adopted to decompose the composite index sequence into intrinsic mode function components (IMF) of different scales and one trend term. Then the LSTM algorithm is used to predicted and model the decomposed sub-sequences. Finally, the forecast result of the composite index is obtained through integration. The empirical results show that the stock market systemic risk index constructed in this paper can effectively identify important risk events within the sample period. In addition, compared with the benchmark model, the EEMD-LSTM model constructed in this paper shows a stronger early warning ability for systemic financial risks in the stock market. [ABSTRACT FROM AUTHOR]