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020 _a9783319260396
_9978-3-319-26039-6
024 7 _a10.1007/978-3-319-26039-6
_2doi
050 4 _aTA352-356
050 4 _aQC20.7.N6
072 7 _aTBJ
_2bicssc
072 7 _aGPFC
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072 7 _aTEC009000
_2bisacsh
072 7 _aTBJ
_2thema
072 7 _aGPFC
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082 0 4 _a515.39
_223
100 1 _aChaudhuri, Arindam.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_958709
245 1 0 _aQuantitative Modeling of Operational Risk in Finance and Banking Using Possibility Theory
_h[electronic resource] /
_cby Arindam Chaudhuri, Soumya K. Ghosh.
250 _a1st ed. 2016.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXVI, 190 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Fuzziness and Soft Computing,
_x1860-0808 ;
_v331
520 _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.
650 0 _aDynamics.
_958710
650 0 _aNonlinear theories.
_93339
650 0 _aStatistics .
_931616
650 0 _aOperations research.
_912218
650 0 _aSocial sciences—Mathematics.
_931863
650 1 4 _aApplied Dynamical Systems.
_932005
650 2 4 _aStatistics in Business, Management, Economics, Finance, Insurance.
_931719
650 2 4 _aOperations Research and Decision Theory.
_931599
650 2 4 _aMathematics in Business, Economics and Finance.
_931864
700 1 _aGhosh, Soumya K.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_958711
710 2 _aSpringerLink (Online service)
_958712
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319260372
776 0 8 _iPrinted edition:
_z9783319260389
776 0 8 _iPrinted edition:
_z9783319374185
830 0 _aStudies in Fuzziness and Soft Computing,
_x1860-0808 ;
_v331
_958713
856 4 0 _uhttps://doi.org/10.1007/978-3-319-26039-6
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c80201
_d80201