Mathematical Problems in Financial Fluctuations and Forecasting

Dear Colleagues,

Financial fluctuations and forecasting are extremely crucial topics in the field of mathematical finance. They are subject to the combined influence of a multitude of factors, such as the volatility of economic indicators, the ups and downs of market sentiment, and the impact of geopolitical events. These uncertain factors are interwoven with each other, jointly constituting the complexity and unpredictability of financial markets. In light of this, to accurately predict financial market fluctuations, there is an urgent need to develop and apply advanced mathematical models that can precisely capture the inherent complex interactions and nonlinear dynamic characteristics of financial markets. These models must not only possess rigor and rationality at the theoretical level but also demonstrate strong robustness in practical applications when faced with a variety of complex and changing market conditions. This enables us to gain a deep understanding, conduct a comprehensive analysis, and make accurate predictions of financial market behavior. Undoubtedly, this is a major core challenge currently faced by the research field of mathematical finance.

The significance of financial fluctuations and forecasting in the field of mathematical finance cannot be overstated, as they play a vital role. They are not only directly related to the stability and operational efficiency of financial markets but also play a crucial role in the decision-making of investors and the risk management of financial institutions. In this key area, the development and application of mathematical models are undoubtedly the cornerstone for understanding, analyzing, and predicting the behavior of financial markets. It is essential to delve into how these cutting-edge mathematical and statistical methods can be effectively applied to the actual analysis of financial markets and to optimize and improve existing financial forecasting models through these methods to better meet the needs of financial market participants. This Special Issue, titled "Mathematical Problems in Financial Fluctuations and Forecasting", is committed to gathering numerous original research articles and professional reviews that focus on the application of mathematical foundations and innovative methods in the dynamic and forecasting analysis of financial markets. It will pay close attention to the mathematical and statistical methods of financial fluctuation modeling, striving to provide new insights and, based on these insights, continuously improve the accuracy of financial forecasting.

The content of the Special Issue will cover a wide range of cutting-edge topics, including stochastic processes, time series analysis, machine learning techniques, and various computational methods specifically applied to financial data. Its core objective is to achieve a comprehensive and in-depth understanding of the mathematical problems closely related to financial fluctuations, offer a more profound interpretation of financial market fluctuations, and actively explore innovative solutions that can significantly enhance forecasting capabilities, in order to inject new vitality into the research and practice of mathematical finance. The submission topics can cover a wide range of areas, including but not limited to the innovative research of mathematical models for financial market fluctuations, innovations in risk assessment methods, the optimal design of investment strategies, and the organic combination with cutting-edge deep learning. Topics of interest include, but are not limited to, the issues presented. We look forward to receiving your contributions.

Prof. Dr. Lu Wang
Guest Editor

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• application of advanced statistical techniques to financial forecasting
• computational methods for solving complex financial problems
• modeling and forecasting of financial market volatility
• high-frequency financial data analysis
• statistical inference
• risk management
• stochastic processes
• analysis of financial volatility forecasting integrating computer technology and mathematical models