Research on Mathematical Modeling and Prediction of Financial Risks

Dear Colleagues,

This Special Issue invites high-quality theoretical and empirical research papers that explore the development and application of mathematical models and analytical techniques in finance. In the past, modeling financial markets through mathematical models has been able to abstract and quantify these complex phenomena, thereby providing a solid theoretical foundation for risk assessment and management. This allows for better identification, quantification, and response to various risks, such as market risk, credit risk, and liquidity risk, thereby enhancing the stability of the financial system. It also provides a scientific basis for pricing complex financial instruments, maximizing returns under controllable risks, and offering powerful tools for predicting financial markets and supporting decision-making. This further demonstrates the importance of mathematical finance modeling and analysis. However, with the increasing complexity and uncertainty of the financial market environment, new challenges continue to emerge. On the one hand, financial markets are subject to multifaceted shocks, including macroeconomic fluctuations, geopolitical conflicts, and sudden public health events. These factors interweave with each other, making market dynamics increasingly unpredictable. On the other hand, the dimensions of data are becoming more diversified, with data sources being widespread and data volumes being large and growing explosively. Meanwhile, high-frequency changes and the non-linear characteristics of data make it difficult for traditional data analysis methods to effectively capture their underlying patterns. In addition, there is the issue of small sample sizes in emerging markets, among others. These factors collectively act to further drive the innovation and research of mathematical finance models. To better address these challenges, it is necessary to develop more flexible, precise, and adaptive mathematical models and to combine them with the latest machine learning technologies in order to enhance the ability to explain and predict market dynamics, thereby providing more robust support for financial decision-making. We welcome both theoretical and empirical studies that address the challenges and opportunities in this field. 

Dr. Chao Liang
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• mathematics and economics
• volatility forecasting analysis
• advanced option pricing models
• sentiment analysis and text mining
• credit risk and portfolio optimization modeling
• time series analysis combined with machine learning modeling
• statistical inference: construction of financial models and derivation of their properties
• interest rate modeling: new approaches to modeling the term structure of interest rates, incorporating market microstructure and macroeconomic factors
• high-frequency data analysis: statistical and econometric methods for analyzing high-frequency financial data, capturing intraday patterns and market microstructure effects
• numerical methods for financial models: development and implementation of efficient numerical methods for solving complex financial models, including finite difference methods, Monte Carlo simulations, and Fourier transform techniques