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Quantitative modeling of operational...

Chaudhuri, Arindam.

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Quantitative modeling of operational risk in finance and banking using possibility theory

Record Type:	Electronic resources : Monograph/item
Title/Author:	Quantitative modeling of operational risk in finance and banking using possibility theory/ by Arindam Chaudhuri, Soumya K. Ghosh.
Author:	Chaudhuri, Arindam.
other author:	Ghosh, Soumya K.
Published:	Cham :Springer International Publishing : : 2016.,
Description:	xvi, 190 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Engineering. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-26039-6
ISBN:	9783319260396$q(electronic bk.)

Quantitative modeling of operational risk in finance and banking using possibility theory
Chaudhuri, Arindam.

Quantitative modeling of operational risk in finance and banking using possibility theory[electronic resource] /by Arindam Chaudhuri, Soumya K. Ghosh. - Cham :Springer International Publishing :2016. - xvi, 190 p. :ill., digital ;24 cm. - Studies in fuzziness and soft computing,v.3311434-9922 ;. - Studies in fuzziness and soft computing ;v.273..

This book offers a comprehensive guide to the modelling of operational risk using possibility theory. It provides a set of methods for measuring operational risks under a certain degree of vagueness and impreciseness, as encountered in real-life data. It shows how possibility theory and indeterminate uncertainty-encompassing degrees of belief can be applied in analysing the risk function, and describes the parametric g-and-h distribution associated with extreme value theory as an interesting candidate in this regard. The book offers a complete assessment of fuzzy methods for determining both value at risk (VaR) and subjective value at risk (SVaR), together with a stability estimation of VaR and SVaR. Based on the simulation studies and case studies reported on here, the possibilistic quantification of risk performs consistently better than the probabilistic model. Risk is evaluated by integrating two fuzzy techniques: the fuzzy analytic hierarchy process and the fuzzy extension of techniques for order preference by similarity to the ideal solution. Because of its specialized content, it is primarily intended for postgraduates and researchers with a basic knowledge of algebra and calculus, and can be used as reference guide for research-level courses on fuzzy sets, possibility theory and mathematical finance. The book also offers a useful source of information for banking and finance professionals investigating different risk-related aspects.

ISBN: 9783319260396$q(electronic bk.)

Standard No.: 10.1007/978-3-319-26039-6doiSubjects--Topical Terms:

586835
Engineering.

LC Class. No.: QA76.9.M35

Dewey Class. No.: 620

Quantitative modeling of operational risk in finance and banking using possibility theory
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100 1 $a Chaudhuri, Arindam. $3 2180020
245 1 0 $a Quantitative modeling of operational risk in finance and banking using possibility theory $h [electronic resource] / $c by Arindam Chaudhuri, Soumya K. Ghosh.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a xvi, 190 p. : $b ill., digital ; $c 24 cm.
490 1 $a Studies in fuzziness and soft computing, $x 1434-9922 ; $v v.331
520 $a This book offers a comprehensive guide to the modelling of operational risk using possibility theory. It provides a set of methods for measuring operational risks under a certain degree of vagueness and impreciseness, as encountered in real-life data. It shows how possibility theory and indeterminate uncertainty-encompassing degrees of belief can be applied in analysing the risk function, and describes the parametric g-and-h distribution associated with extreme value theory as an interesting candidate in this regard. The book offers a complete assessment of fuzzy methods for determining both value at risk (VaR) and subjective value at risk (SVaR), together with a stability estimation of VaR and SVaR. Based on the simulation studies and case studies reported on here, the possibilistic quantification of risk performs consistently better than the probabilistic model. Risk is evaluated by integrating two fuzzy techniques: the fuzzy analytic hierarchy process and the fuzzy extension of techniques for order preference by similarity to the ideal solution. Because of its specialized content, it is primarily intended for postgraduates and researchers with a basic knowledge of algebra and calculus, and can be used as reference guide for research-level courses on fuzzy sets, possibility theory and mathematical finance. The book also offers a useful source of information for banking and finance professionals investigating different risk-related aspects.
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