Mathematical and Statistical Methods for Actuarial Sciences and Finance: MAF2024

ISBN 10: 3031642724 / ISBN 13: 9783031642722

Editore: Springer, 2024

Riguardo questo articolo

SUPER FAST SHIPPING.

Codice articolo 9783031642722

Dati bibliografici

• Titolo: Mathematical and Statistical Methods for Actuarial Sciences and Finance: MAF2024
• Editore: Springer
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 10: 3031642724
• ISBN 13: 9783031642722
• Rilegatura: Rilegato
• Condizione: New

Descrizione articolo

Riassunto

The book presents a collection of peer-reviewed short papers selected from those presented at the International Conference Mathematical and Statistical Methods for Actuarial Sciences and Finance – MAF2024. The year 2024 marks the twentieth anniversary of the first edition of this conference.

The idea behind this book is that collaboration and cross-pollination between mathematicians and statisticians working in actuarial sciences and finance could enhance research in these fields. The effectiveness of this concept has been demonstrated by widespread participation across all editions, held at various locations including the University of Salerno, Italy (2004, 2006, 2010, 2014, and 2022); Ca' Foscari University of Venice, Italy (2008, 2012, and 2020); University Paris-Dauphine in Paris, France (2016); University Carlos III of Madrid, Madrid (2018); and University of Le Havre Normandie, Le Havre, France (2024). This effectiveness is also evident in the attention consistently shown by both the scientific community and professionals toward the volumes of peer-reviewed papers accompanying all past editions of MAF.

The book is a valuable resource for academics, researchers, Ph.D. students, and professionals. Furthermore, it is also of interest to other readers with a quantitative background.

Informazioni sull?autore

Marco Corazza is graduated with honors in “Economics and Business” and obtained the Ph.D. in “Mathematics for the Analysis of Financial Markets.” Currently, he is an associate professor at the Department of Economics at the Ca 'Foscari University of Venice. His main research interests are quantitative finance, artificial intelligence and machine learning for economics and finance, bio-inspired computational methodologies for optimization, and multicriteria methods for decision support. He participated in national and international research projects. The results of his research appeared in more than one hundred and fifty publications of national and international relevance. Parallel to academic activity, it carries out consultancy activities.