Quantitative Modeling of Operational Risk in Finance and Banking Using Possibility Theory (Studies in Fuzziness and Soft Computing, 331, Band 331) - Hardcover

Buch 99 von 183: Studies in Fuzziness and Soft Computing

Chaudhuri, Arindam; Ghosh, Soumya K.

 
9783319260372: Quantitative Modeling of Operational Risk in Finance and Banking Using Possibility Theory (Studies in Fuzziness and Soft Computing, 331, Band 331)
Hardcover
ISBN 10: 3319260375 ISBN 13: 9783319260372
Verlag: Springer, 2015

Inhaltsangabe

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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Über die Autorin bzw. den Autor

Arindam Chaudhuri is currently working as Principal Data Scientist at the Samsung R & D Institute in Delhi, India. He has worked in industry, research, and academics in the domain of machine learning for the past 19 years. His current research interests include pattern recognition, machine learning, soft computing, optimization, and big data. He received his M.Tech and PhD in Computer Science from Jadavpur University, Kolkata, India and Netaji Subhas University, Kolkata, India in 2005 and 2011 respectively. He has published three research monographs and over 45 articles in international journals and conference proceedings.

Von der hinteren Coverseite

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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Verlag
Springer
Erscheinungsdatum
2015
Sprache
Englisch
ISBN 10 
3319260375
ISBN 13 
9783319260372
Einband
Gebundene Ausgabe
Auflage
1
Anzahl der Seiten
206
Kontakt zum Hersteller
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Darmstadt
64293
Deutschland

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9783319374185: Quantitative Modeling of Operational Risk in Finance and Banking Using Possibility Theory (Studies in Fuzziness and Soft Computing, Band 331)

Vorgestellte Ausgabe

ISBN 10:  3319374184 ISBN 13:  9783319374185
Verlag: Springer, 2016
Softcover