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Heavy-Tailed Distributions and Robustness in Economics and Finance electronic resource by Marat Ibragimov, Rustam Ibragimov, Johan Walden.

By: Ibragimov, Marat [author.]Contributor(s): Ibragimov, Rustam [author.] | Walden, Johan [author.] | SpringerLink (Online service)Material type: TextTextSeries: Lecture Notes in StatisticsPublication details: Cham : Springer International Publishing : Imprint: Springer, 2015Description: XIV, 119 p. 9 illus. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783319168777Subject(s): Statistics | Econometrics | Statistics | Statistics for Business/Economics/Mathematical Finance/Insurance | Statistical Theory and Methods | EconometricsDDC classification: 330.015195 LOC classification: QA276-280Online resources: Click here to access online
Contents:
Introduction -- Implications of Heavy-tailed ness -- Inference and Empirical Examples -- Conclusion.
In: Springer eBooksSummary: 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.
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Introduction -- Implications of Heavy-tailed ness -- Inference and Empirical Examples -- Conclusion.

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.

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