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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 models Methods 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.
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 models Methods 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.
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