Studies from University of Genoa in the Area of Insurance Reported (Combination of Autoregressive Graphical Models and Time Series Bootstrap Methods for Risk Management In Marine Insurance): Insurance

2024 APR 29 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Fresh data on Insurance are presented in a new report. According to news reporting out of Genoa, Italy, by NewsRx editors, research stated, “In this paper a methodology to assess risk by forecasting the trend of marine losses at a global scale is presented. The proposed procedure, which can be used to continuously update an insurance company’s costing model, identifies the most relevant risk indicators through Probabilistic Graphical Models (PGMs).”

Funders for this research include Swiss Re Corporate Solutions, Ministry of Education, Universities and Research (MIUR), INDAM-GNAMPA group.

Our news journalists obtained a quote from the research from the University of Genoa, “The use of PGMs makes the variable selection more understandable since they provide a clear interface to interpret the model and perform predictions. Furthermore, this procedure can be used to verify independence relationships, validate the dataset and identify unexpected links among the considered variables.”

According to the news editors, the research concluded: “The robustness of estimates, crucial for risk assessment in the insurance context, is dealt with bootstrap.”

This research has been peer-reviewed.

For more information on this research see: Combination of Autoregressive Graphical Models and Time Series Bootstrap Methods for Risk Management In Marine Insurance. Socio-economic Planning Sciences, 2024;92. Socio-economic Planning Sciences can be contacted at: Elsevier Science Inc, Ste 800, 230 Park Ave, New York, NY 10169, USA. (Elsevier - www.elsevier.com; Socio-economic Planning Sciences - http://www.journals.elsevier.com/socio-economic-planning-sciences/)

Our news journalists report that additional information may be obtained by contacting Francesco Porro, University of Genoa, Dipartimento Matemat, Dipartimento Eccellenza 2023 2027, Via Dodecaneso 35, I-16146 Genoa, Italy. Additional authors for this research include Federico Carli, Eva Riccomagno and Elena Pesce.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.seps.2024.101833. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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