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 Ca Foscari University of Venice, and
was held from 20 to 22 April 2022 in Salerno (Italy) 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.
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