Applied Mathematics in Financial Markets and Risk Analysis
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E5: Financial Mathematics".
Deadline for manuscript submissions: 28 February 2027 | Viewed by 52
Special Issue Editor
Special Issue Information
Dear Colleagues,
Since the consolidation of quantitative finance as a central discipline within applied mathematics, there has been sustained growth in the development of rigorous mathematical models and computational techniques for the analysis of financial markets and risk. These approaches have been widely adopted in both academic research and industry due to their capacity to address the inherent complexity, uncertainty, and high-dimensional nature of modern financial systems.
On the one hand, mathematical tools such as stochastic processes, optimization methods, and numerical analysis provide solid theoretical foundations for modeling asset dynamics, pricing financial instruments, and assessing risk. On the other hand, recent advances in machine learning and neural networks have introduced powerful data-driven methodologies capable of extracting structure from large and complex datasets, thereby enhancing forecasting, classification, and decision-making processes in finance.
In this framework, statistical arbitrage has gained increasing relevance as a systematic approach to identifying market inefficiencies through probabilistic and statistical modeling. These methodologies have been applied in asset pricing, portfolio optimization, risk management, and algorithmic trading.
This Special Issue aims to provide a forum for high-quality contributions that present novel theoretical developments and applied results in the field. It seeks to promote further research at the interface of applied mathematics, data science, and financial analysis.
Prof. Dr. José Pedro Ramos-Requena
Guest Editor
Manuscript Submission Information
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Keywords
- stochastic processes and stochastic differential equations in finance
- optimization methods for portfolio selection and asset allocation
- numerical methods for derivative pricing
- risk modeling and quantitative risk management
- statistical arbitrage and market efficiency
- machine learning and neural networks in finance
- deep learning for financial forecasting and trading strategies
- high-frequency data analysis and algorithmic trading
- volatility modeling and forecasting
- systemic risk and financial network models
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