Mathematical Modelling in Economics and Finance
A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Mathematics and Finance".
Deadline for manuscript submissions: 31 December 2026 | Viewed by 41
Special Issue Editor
2. Department of Finance, IPAG Business School, IPAG LAB, 184 Boulevard Saint-Germain, 75006 Paris, France
Interests: econometric theory; financial econometrics; time series and panel data econometrics; applied mathematics; artificial intelligence methods; signal processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Mathematical modelling in economics and finance still remains an essential tool allowing the explanation of several phenomena appearing on different financial markets. The development of mathematical and probabilistic models for finance and economics has made it possible to progress in the field of forecasting under turbulent and uncertain conditions. Indeed, research on stochastic processes and probability models has provided a natural set of applications for the vast array of tools and results developed in business and economics theory. Nevertheless, mathematical finance has also been a source of new research questions and challenges in recent years that have generated new motivations and momentum for research on stochastic processes. The main objective of this Special Issue is to approve new approaches to stochastic modelling and estimate density function that deepen our insights into modelling and predicting techniques. This Special Issue aims at collecting original contributions containing new theoretical and/or empirical results in the context of economic and finance modelling, estimating and forecasting using mathematical, statistical, and econometric implements. However, effective alternative approaches are also encouraged.
Dr. Heni Boubaker
Guest Editor
Manuscript Submission Information
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Keywords
- stochastic process
- density estimation
- mathematical finance
- modelling methods
- forecasting approach
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