Innovative Quantitative Methods for Financial Risk Management
A special issue of Risks (ISSN 2227-9091).
Deadline for manuscript submissions: 31 May 2025 | Viewed by 163
Special Issue Editor
Interests: risk modelling, in particular tail risk; quantitative risk management; financial mathematics; machine learning and data analytics; applied probability (change of measure, urn models, etc.)
Special Issue Information
Dear Colleagues,
Traditionally, financial risk management has been pivotal for safeguarding assets, ensuring stability and fostering growth in the volatile world of finance, with a tremendously positive impact on the real economy as well. The use of quantitative methods has proven essential to assess and hedge risks meaningfully. Research in stochastic calculus, the theory of risk measures, advanced simulations and numerical mathematics has provided invaluable results, which are now part of the panoply of tools we rely upon in dealing with financial risks and risks in general.
However, as the financial landscape evolves, so does the complexity of risks, necessitating newer and more reliable modeling approaches. This Special Issue seeks to bridge traditional methodologies with innovative approaches, inviting groundbreaking contributions that push the boundaries of quantitative risk management. In addition to new findings in traditional areas of quantitative risk management, we particularly welcome alternative approaches and research exploring the integration of machine learning algorithms. Decentralized finance and blockchains, as tools for risk mitigation and transparency, are also of interest for this Special Issue when studied from a quantitative point of view.
I look forward to receiving your contributions. Please feel free to reach out if you have article proposals.
Best regards,
Prof. Dr. Pasquale Cirillo
Guest Editor
Manuscript Submission Information
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Keywords
- quantitative risk management
- financial risk
- financial markets
- financial mathematics
- decentralized finance
- blockchain
- artificial intelligence
- machine learning
- risk measures
- stochastic processes
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