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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-tailedness. 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.

Statistics. --- Statistics for Business/Economics/Mathematical Finance/Insurance. --- Statistical Theory and Methods. --- Econometrics. --- Mathematical statistics. --- Economics --- Statistique --- Statistique mathématique --- Econométrie --- Distribution (Probability theory). --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- Distribution (Probability theory) --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- Statistics for Business, Management, Economics, Finance, Insurance. --- Economics, Mathematical --- Statistics --- Statistical inference --- Statistics, Mathematical --- Sampling (Statistics) --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Econometrics --- Statistics .