Hierarchical Archimedean Copulas - Górecki, Jan;Okhrin, Ostap
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This book offers a thorough understanding of Hierarchical Archimedean Copulas (HACs) and their practical applications. It covers the basics of copulas, explores the Archimedean family, and delves into the specifics of HACs, including their fundamental properties. The text also addresses sampling algorithms, HAC parameter estimation, and structure, and highlights temporal models with applications in finance and economics. The final chapter introduces R, MATLAB, and Octave toolboxes for copula modeling, enabling students, researchers, data scientists, and practitioners to model complex…mehr

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Produktbeschreibung
This book offers a thorough understanding of Hierarchical Archimedean Copulas (HACs) and their practical applications. It covers the basics of copulas, explores the Archimedean family, and delves into the specifics of HACs, including their fundamental properties. The text also addresses sampling algorithms, HAC parameter estimation, and structure, and highlights temporal models with applications in finance and economics. The final chapter introduces R, MATLAB, and Octave toolboxes for copula modeling, enabling students, researchers, data scientists, and practitioners to model complex dependence structures and make well-informed decisions across various domains.
Autorenporträt
Jan Górecki is an Assistant Professor at the Department of Informatics and Mathematics, School of Business Administration in Karviná, at the Silesian University in Opava, Czech Republic. He is engaged in research in the field of computational statistics, machine learning and large language models, and teaching courses focused on artificial intelligence and web development. Ostap Okhrin is Professor of Econometrics and Statistics, especially in Transportation, at the Institute of Transport and Economics, TU Dresden, Germany. He has co-authored nearly 100 publications in the field of mathematical and applied statistics, econometrics, and reinforcement learning, with applications to finance, economics and autonomous driving.