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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.

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Introduction.- Implications of Heavy-tailed ness.- Inference and Empirical Examples.- Conclusion.

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Since 2013, Marat Ibragimov works as an Associate Professor at Kazan (Volga Region) Federal University in Kazan, Russia. He graduated from the Department of Mathematics at Kazan State University in 1974 and received his Ph.D. from the Uzbek Academy of Sciences in 1982. His current research interests include econometric analysis of emerging, transition and post-Soviet markets, the study of income and wealth distribution and labour markets in emerging and transition countries, modelling of financial and economic crises, matrix theory and moment and probability inequalities, among others.

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