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.

Offers a comprehensive report on the modeling of operational risk using possibility theory Features a set of methods for analyzing operational risk using fuzzy set theory Presents advanced concepts in fuzzy sets, possibility theory and mathematical finance Includes supplementary material: sn.pub/extras

Autorentext

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.

Inhalt

Introduction.- Operational Risk.- The g-and-h Distribution.- Probabilistic view of Operational Risk.- Possibility Theory for Operational Risk.- Possibilistic View of Operational Risk.- Simulation Results.- A case study: Iron ore Mining in India.- Evaluation of the Possibilistic Quantification of Operational Risk.- Summary and Future Research.