大葉大學圖書館 |

Quantitative modeling of operational risk in finance and banking using possibility theory[electronic resource] /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	620
書名/作者:	Quantitative modeling of operational risk in finance and banking using possibility theory/ by Arindam Chaudhuri, Soumya K. Ghosh.
作者:	Chaudhuri, Arindam.
其他作者:	Ghosh, Soumya K.
出版者:	Cham : : Springer International Publishing :, 2016.
面頁冊數:	xvi, 190 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Engineering.
標題:	Operations research.
標題:	Decision making.
標題:	Economics, Mathematical.
標題:	Statistics.
標題:	Computational complexity.
標題:	Complexity.
標題:	Statistics for Business/Economics/Mathematical Finance/Insurance.
標題:	Operation Research/Decision Theory.
標題:	Quantitative Finance.
ISBN:	9783319260396
ISBN:	9783319260372
摘要、提要註:	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.
電子資源:	http://dx.doi.org/10.1007/978-3-319-26039-6

Quantitative modeling of operational risk in finance and banking using possibility theory[electronic resource] /
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

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

372756
Engineering.

LC Class. No.: QA76.9.M35

Dewey Class. No.: 620

Quantitative modeling of operational risk in finance and banking using possibility theory[electronic resource] /
LDR:02503nam a2200313 a 4500 001 454983
003 DE-He213
005 20160722110929.0
006 m d
007 cr nn 008maaau
008 161227s2016 gw s 0 eng d
020 $a 9783319260396 $q (electronic bk.)
020 $a 9783319260372 $q (paper)
024 7 $a 10.1007/978-3-319-26039-6 $2 doi
035 $a 978-3-319-26039-6
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.M35
072 7 $a GPFC $2 bicssc
072 7 $a TEC000000 $2 bisacsh
082 0 4 $a 620 $2 23
090 $a QA76.9.M35 $b C496 2016
100 1 $a Chaudhuri, Arindam. $3 652951
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.
650 0 $a Engineering. $3 372756
650 0 $a Operations research. $3 172182
650 0 $a Decision making. $3 337308
650 0 $a Economics, Mathematical. $3 227334
650 0 $a Statistics. $3 145349
650 0 $a Computational complexity. $3 393856
650 2 4 $a Complexity. $3 464233
650 2 4 $a Statistics for Business/Economics/Mathematical Finance/Insurance. $3 464928
650 2 4 $a Operation Research/Decision Theory. $3 511310
650 2 4 $a Quantitative Finance. $3 464461
700 1 $a Ghosh, Soumya K. $3 652952
710 2 $a SpringerLink (Online service) $3 463450
773 0 $t Springer eBooks
830 0 $a Studies in fuzziness and soft computing ; $v v.273. $3 465430
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-26039-6
950 $a Engineering (Springer-11647)