The cooperation and contamination among mathematicians,
statisticians and econometricians working in actuarial sciences and
finance are improving the research on these topics and producing
numerous meaningful scientific results. This volume presents new
ideas in the form of four- to six-page papers presented at the
International Conference MAF2022 – Mathematical and Statistical
Methods for Actuarial Sciences and Finance. Due to the COVID-19
pandemic, the conference, to which this book is related, was
organized in a hybrid form by the Department of Economics and
Statistics of the University of Salerno, with the partnership of
the Department of Economics of CĂ Foscari University of
Venice, and was held from 20 to 22 April 2022 in Salerno
(Italy)Â MAF2022 is the tenth edition of an international
biennial series of scientific meetings, started in 2004 on the
initiative of the Department of Economics and Statistics of the
University of Salerno. It has established itself internationally
with gradual and continuous growth and scientific enrichment. The
effectiveness of this idea has been proven by the wide
participation in all the editions, which have been held in Salerno
(2004, 2006, 2010, 2014, 2022), Venice (2008, 2012 and 2020
online), Paris (2016) and Madrid (2018). This book covers a wide
variety of subjects: artificial intelligence and machine learning
in finance and insurance, behavioural finance, credit risk methods
and models, dynamic optimization in finance, financial data
analytics, forecasting dynamics of actuarial and financial
phenomena, foreign exchange markets, insurance models, interest
rate models, longevity risk, models and methods for financial time
series analysis, multivariate techniques for financial markets
analysis, pension systems, portfolio selection and management,
real-world finance, risk analysis and management, trading systems,
and others. This volume is a valuable resource for academics, PhD
students, practitioners, professionals and researchers. Moreover,
it is also of interest to other readers with quantitative
background knowledge.
General
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