Special Issue "Time Series Modeling for Finance and Insurance"
A special issue of Risks (ISSN 2227-9091).
Deadline for manuscript submissions: closed (25 August 2023) | Viewed by 5390
Interests: computational finance; option pricing; stochastic processes; stochastic mortality models
Recent developments in the insurance and financial industry call for appropriate forecasting techniques. The wide set of dependence structures of the financial and insurance datasets has required a rapid development of complex dynamic models and, at same time, has given a boost to the search for new methodologies. Standard discrete and continuous-time models have been extended in order to be able to reproduce well known stylized facts of financial and insurance time series and to reduce forecasting error.
In this context, this Special Issue is devoted to the collection of the latest developments on topics from many areas, such as modelling of time series, machine learning, evaluation of financial/insurance contracts, stochastic volatility modelling, and modelling of big data. Codes and/or pseudocodes for the used algorithms/estimation procedures will be made available.
Prof. Dr. Lorenzo Mercuri
Dr. Edit Rroji
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Risks is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- time-series modeling
- mathematical finance
- machine learning