New Challenges in Mathematical Finance: From S(P)DEs to Machine Learning

Dear Colleagues,

Financial markets are currently characterized by an increasing number of challenging tasks which outperform already-obtained results in terms, for example, of standard credit risk measures, interest rates structures analysis, financial scenario forecasting, and related sub-problems. In particular, the enormous mass of data, spanning from micro-economic to macro-economic frameworks, demands the development of a new stochastic-based holistic vision which can re-establish the right grip between mathematical theory and related applied tools as well as between quantitative tasks and their formally correct description/analysis/forecast. Therefore, this Special Issue will be devoted to collecting contributions aimed at finding a collective approach to the aforementioned challenges. It welcomes papers dealing with concrete financial problems from different perspectives. Contributions related to, for example,  the theory of SPDEs, stochastic mean field games, neural networks solutions, and combinations of these will constitute the core of the present Special Issue, yet not exhausting the set of possible mathematical techniques and/or specific financial models to be analyzed, with special attention paid to those derived from real-world scenarios (e.g., the prediction of production/consumption in energy markets, interest rates structuring, clustering and optimization in productive processes, the quest for effective numerical schemes for SPDEs-based stock-option dynamics, etc.).

Dr. Luca Di Persio
Dr. Francesco Giuseppe Cordoni
Dr. Immacolata Oliva
Guest Editors

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