Quantitative Modeling of Operational Risk in Finance and Banking Using Possibility Theory

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 indeterminat...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς: Chaudhuri, Arindam (Συγγραφέας), Ghosh, Soumya K. (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2016.
Σειρά:Studies in Fuzziness and Soft Computing, 331
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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100 1 |a Chaudhuri, Arindam.  |e author. 
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. 
264 1 |a Cham :  |b Springer International Publishing :  |b Imprint: Springer,  |c 2016. 
300 |a CXC, 16 p.  |b online resource. 
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490 1 |a Studies in Fuzziness and Soft Computing,  |x 1434-9922 ;  |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. 
650 0 |a Engineering. 
650 0 |a Operations research. 
650 0 |a Decision making. 
650 0 |a Economics, Mathematical. 
650 0 |a Statistics. 
650 0 |a Complexity, Computational. 
650 1 4 |a Engineering. 
650 2 4 |a Complexity. 
650 2 4 |a Statistics for Business/Economics/Mathematical Finance/Insurance. 
650 2 4 |a Operation Research/Decision Theory. 
650 2 4 |a Quantitative Finance. 
700 1 |a Ghosh, Soumya K.  |e author. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9783319260372 
830 0 |a Studies in Fuzziness and Soft Computing,  |x 1434-9922 ;  |v 331 
856 4 0 |u http://dx.doi.org/10.1007/978-3-319-26039-6  |z Full Text via HEAL-Link 
912 |a ZDB-2-ENG 
950 |a Engineering (Springer-11647)