Computational Finance and Risk Analysis in Insurance II
A special issue of Risks (ISSN 2227-9091).
Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 16058
Special Issue Editor
2. Department Financial Mathematics, Fraunhofer ITWM, Fraunhofer-Platz 1, 67663 Kaiserslautern, Germany
Interests: portfolio optimization; stochastic control in finance and insurance; risk-return assessment to financial products; Monte Carlo simulation; tree methods; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Whilst developing valuation concepts for financial products, modelling of financial processes, risk measurement issues and portfolio optimization are often central aspects of research, the computational methods to produce the final numbers are equally important in the application of financial and insurance mathematics.
With this Special Issue I would like to encourage all colleagues (from both academia and industry) working in the computational area of finance and insurance to share their innovative methods with the community. These methods can be (but are not limited to) the following:
- variants of classical computational approaches such as Monte Carlo algorithms, tree methods, quadrature or methods to solve partial differential equations,
- new machine learning methods, in particular neural network approaches,
- algorithms from computational statistics,
- specialized algorithms to deal with an important practical issue.
The Special Issue favours contributions that are closely related to a specific application in real life, but also theoretical contributions that e.g. deal with the convergence or speed up of well-established methods are welcome. Survey papers on areas of computational finance might also be acceptable, but should only be handed in after having contacted me.
Prof. Dr. Ralf Korn
Guest Editor
Manuscript Submission Information
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Keywords
- Monte Carlo methods
- tree methods and algorithms for PDE related to finance/insurance
- risk assessment
- machine learning methods
- neural networks
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