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024 7 $a10.1007/978-3-319-26039-6
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100   $a20151031d2016      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt
182   $cc
183   $acr
200 10$aQuantitative Modeling of Operational Risk in Finance and Banking Using Possibility Theory /$fby Arindam Chaudhuri, Soumya K. Ghosh
205   $a1st ed. 2016.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016.
215   $a1 online resource (XVI, 190 p. 65 illus., 53 illus. in color.) 
225 1 $aStudies in Fuzziness and Soft Computing,$x1434-9922 ;$v331
300   $aBibliographic Level Mode of Issuance: Monograph
311   $a3-319-26037-5 
330   $aThis book offers a comprehensive guide to the modelling of operational risk using possibility theory. It provides a set of methods for measuring operational risks under a certain degree of vagueness and impreciseness, as encountered in real-life data. It shows how possibility theory and indeterminate uncertainty-encompassing degrees of belief can be applied in analysing the risk function, and describes the parametric g-and-h distribution associated with extreme value theory as an interesting candidate in this regard. The book offers a complete assessment of fuzzy methods for determining both value at risk (VaR) and subjective value at risk (SVaR), together with a stability estimation of VaR and SVaR. Based on the simulation studies and case studies reported on here, the possibilistic quantification of risk performs consistently better than the probabilistic model. Risk is evaluated by integrating two fuzzy techniques: the fuzzy analytic hierarchy process and the fuzzy extension of techniques for order preference by similarity to the ideal solution. Because of its specialized content, it is primarily intended for postgraduates and researchers with a basic knowledge of algebra and calculus, and can be used as reference guide for research-level courses on fuzzy sets, possibility theory and mathematical finance. The book also offers a useful source of information for banking and finance professionals investigating different risk-related aspects.
410  0$aStudies in Fuzziness and Soft Computing,$x1434-9922 ;$v331
606   $aComputational complexity
606   $aStatistics 
606   $aOperations research
606   $aDecision making
606   $aEconomics, Mathematical 
606   $aComplexity$3https://scigraph.springernature.com/ontologies/product-market-codes/T11022
606   $aStatistics for Business, Management, Economics, Finance, Insurance$3https://scigraph.springernature.com/ontologies/product-market-codes/S17010
606   $aOperations Research/Decision Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/521000
606   $aQuantitative Finance$3https://scigraph.springernature.com/ontologies/product-market-codes/M13062
615  0$aComputational complexity.
615  0$aStatistics .
615  0$aOperations research.
615  0$aDecision making.
615  0$aEconomics, Mathematical .
615 14$aComplexity.
615 24$aStatistics for Business, Management, Economics, Finance, Insurance.
615 24$aOperations Research/Decision Theory.
615 24$aQuantitative Finance.
676   $a658.155
700   $aChaudhuri$b Arindam$4aut$4http://id.loc.gov/vocabulary/relators/aut$0763017
702   $aGhosh$b Soumya K$4aut$4http://id.loc.gov/vocabulary/relators/aut
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910254185603321
996   $aQuantitative Modeling of Operational Risk in Finance and Banking Using Possibility Theory$92496894
997   $aUNINA