9781032477572 - Quantitative Operational Risk Models (Chapman & Hall/CRC Finance Series) de Bolancé, Catalina; Guillén, Montserrat; Gustafsson, Jim; Nielsen, Jens Perch (20 resultados)

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Paperback. Condición: new. Paperback. Using real-life examples from the banking and insurance industries, Quantitative Operational Risk Models details how internal data can be improved based on external information of various kinds. Using a simple and intuitive methodology based on classical transformation methods, the book includes real-life examples of the combination of internal data and external information.A guideline for practitioners, the book begins with the basics of managing operational risk data to more sophisticated and recent tools needed to quantify the capital requirements imposed by operational risk. The book then covers statistical theory prerequisites, and explains how to implement the new density estimation methods for analyzing the loss distribution in operational risk for banks and insurance companies. In addition, it provides: Simple, intuitive, and general methods to improve on internal operational risk assessment Univariate event loss severity distributions analyzed using semiparametric modelsMethods for the introduction of underreporting information A practical method to combine internal and external operational risk data, including guided examples in SAS and R Measuring operational risk requires the knowledge of the quantitative tools and the comprehension of insurance activities in a very broad sense, both technical and commercial. Presenting a nonparametric approach to modeling operational risk data, Quantitative Operational Risk Models offers a practical perspective that combines statistical analysis and management orientations. Presenting a nonparametric approach to modeling operational risk data, this book offers a practical perspective that combines statistical analysis and management orientations. It covers the statistical theory prerequisites and summarizes important contributions made in the past decade. The authors explain how to implement the new density estimat Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Condición: New.

Condición: New.

Paperback / softback. Condición: New. New copy - Usually dispatched within 4 working days.

Condición: New.

Condición: New. 1st edition NO-PA16APR2015-KAP.

Condición: As New. Unread book in perfect condition.

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Condición: New.

Paperback. Condición: Brand New. 236 pages. 9.19x6.13x0.55 inches. In Stock.

Paperback. Condición: new. Paperback. Using real-life examples from the banking and insurance industries, Quantitative Operational Risk Models details how internal data can be improved based on external information of various kinds. Using a simple and intuitive methodology based on classical transformation methods, the book includes real-life examples of the combination of internal data and external information.A guideline for practitioners, the book begins with the basics of managing operational risk data to more sophisticated and recent tools needed to quantify the capital requirements imposed by operational risk. The book then covers statistical theory prerequisites, and explains how to implement the new density estimation methods for analyzing the loss distribution in operational risk for banks and insurance companies. In addition, it provides: Simple, intuitive, and general methods to improve on internal operational risk assessment Univariate event loss severity distributions analyzed using semiparametric modelsMethods for the introduction of underreporting information A practical method to combine internal and external operational risk data, including guided examples in SAS and R Measuring operational risk requires the knowledge of the quantitative tools and the comprehension of insurance activities in a very broad sense, both technical and commercial. Presenting a nonparametric approach to modeling operational risk data, Quantitative Operational Risk Models offers a practical perspective that combines statistical analysis and management orientations. Presenting a nonparametric approach to modeling operational risk data, this book offers a practical perspective that combines statistical analysis and management orientations. It covers the statistical theory prerequisites and summarizes important contributions made in the past decade. The authors explain how to implement the new density estimat Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Taschenbuch. Condición: Neu. Quantitative Operational Risk Models | Catalina Bolancé (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2023 | Chapman and Hall/CRC | EAN 9781032477572 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.

PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Using real-life examples from the banking and insurance industries, Quantitative Operational Risk Models details how internal data can be improved based on external information of various kinds. Using a simple and intuitive methodology based on classical transformation methods, the book includes real-life examples of the combination of internal data and external information.A guideline for practitioners, the book begins with the basics of managing operational risk data to more sophisticated and recent tools needed to quantify the capital requirements imposed by operational risk. The book then covers statistical theory prerequisites, and explains how to implement the new density estimation methods for analyzing the loss distribution in operational risk for banks and insurance companies. In addition, it provides:Simple, intuitive, and general methods to improve on internal operational risk assessment Univariate event loss severity distributions analyzed using semiparametric modelsMethods for the introduction of underreporting information A practical method to combine internal and external operational risk data, including guided examples in SAS and RMeasuring operational risk requires the knowledge of the quantitative tools and the comprehension of insurance activities in a very broad sense, both technical and commercial. Presenting a nonparametric approach to modeling operational risk data, Quantitative Operational Risk Models offers a practical perspective that combines statistical analysis and management orientations. 238 pp. Englisch.

Paperback / softback. Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Catalina Bolance has been Associate Professor of Quantitative Methods for Economics and Management Science of the Department of Econometrics at University of Barcelona since 2001. She received a PhD in Economics, an M.A. in Marketing, an.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Using real-life examples from the banking and insurance industries, Quantitative Operational Risk Models details how internal data can be improved based on external information of various kinds. Using a simple and intuitive methodology based on classical transformation methods, the book includes real-life examples of the combination of internal data and external information.A guideline for practitioners, the book begins with the basics of managing operational risk data to more sophisticated and recent tools needed to quantify the capital requirements imposed by operational risk. The book then covers statistical theory prerequisites, and explains how to implement the new density estimation methods for analyzing the loss distribution in operational risk for banks and insurance companies. In addition, it provides:Simple, intuitive, and general methods to improve on internal operational risk assessment Univariate event loss severity distributions analyzed using semiparametric modelsMethods for the introduction of underreporting information A practical method to combine internal and external operational risk data, including guided examples in SAS and RMeasuring operational risk requires the knowledge of the quantitative tools and the comprehension of insurance activities in a very broad sense, both technical and commercial. Presenting a nonparametric approach to modeling operational risk data, Quantitative Operational Risk Models offers a practical perspective that combines statistical analysis and management orientations.