Ebook: Heavy-Tailed Distributions and Robustness in Economics and Finance

Shows the economic consequences of observed heavy-tailed risk distributions in the fields of economics, finance and insurance Aims to bridge the gap between economic modeling and the statistical modeling techniques that have been developed for observed real-world heavy-tailed risk distributions Offers an integrated and unified treatment of several of the authors' models within the fields of economics, finance and insurance Introduces the concepts and methods in a less technical language also for the non-specialist reader interested in these fields

This book focuses on general frameworks for modeling heavy-tailed distributions in economics, finance, econometrics, statistics, risk management and insurance. A central theme is that of (non-)robustness, i.e., the fact that the presence of heavy tails can either reinforce or reverse the implications of a number of models in these fields, depending on the degree of heavy-tailed ness. These results motivate the development and applications of robust inference approaches under heavy tails, heterogeneity and dependence in observations. Several recently developed robust inference approaches are discussed and illustrated, together with applications.

Topics Statistics for Business, Economics, Mathematical Finance, Insurance Statistical Theory and Methods Econometrics