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Titlebook: Mathematical and Statistical Methods for Actuarial Sciences and Finance; eMAF2020 Marco Corazza,Manfred Gilli,Marilena Sibillo Conference p

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书目名称Mathematical and Statistical Methods for Actuarial Sciences and Finance
副标题eMAF2020
编辑Marco Corazza,Manfred Gilli,Marilena Sibillo
视频video
概述Non-dispersive papers on quantitative studies in actuarial sciences, insurance and finance.Researches jointly developed by mathematician and statisticians.A vast community of reference interested in s
图书封面Titlebook: Mathematical and Statistical Methods for Actuarial Sciences and Finance; eMAF2020 Marco Corazza,Manfred Gilli,Marilena Sibillo Conference p
描述.The cooperation and contamination between mathematicians, statisticians and econometricians working in actuarial sciences and finance is 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 eMAF2020 – Mathematical and Statistical Methods for Actuarial Sciences and Finance.. Due to the now sadly famous COVID-19 pandemic, the conference was held remotely through the Zoom platform offered by the Department of Economics of the Ca’ Foscari University of Venice on September 18, 22 and 25, 2020...eMAF2020. is the ninth edition of an international biennial series of scientific meetings, started in 2004 at the initiative of the Department of Economics and Statistics of the University of Salerno. The effectiveness of this idea has been proven by wide participation in all editions, which have been held in Salerno (2004, 2006, 2010 and 2014), Venice (2008, 2012 and 2020), Paris (2016) and Madrid (2018)...This book covers a wide variety of subjects: artificial intelligence and machine learning in finance and insurance, behavioral finance, c
出版日期Conference proceedings 2021
关键词Actuarial Sciences; Quantitative Insurance; Quantitative Finance; Mathematics; Statistics
版次1
doihttps://doi.org/10.1007/978-3-030-78965-7
isbn_softcover978-3-030-78967-1
isbn_ebook978-3-030-78965-7
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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An Empirical Investigation of Heavy Tails in Emerging Markets and Robust Estimation of the Pareto Ttional Threshold Accepting-VaR based algorithm (TAVaR) for optimally estimating the Pareto tail index. A Monte Carlo bias estimation analysis is also carried out by comparing our proposed methodology with the Hill estimator and a variant of it.
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,Conditional Quantile Estimation for Linear ARCH Models with MIDAS Components,MIDAS (Q–ARCH–MIDAS), allows to benefit from the information coming from variables observed at different frequencies with respect to that of the variable of interest. Moreover, the QR context brings additional advantages, such as the robustness to the presence of outliers and the lack of distributional assumptions.
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