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Synopsis
The management of operational risk in the banking industry has undergone explosive changes over the last decade due to substantial changes in the operational environment. Globalization, deregulation, the use of complex financial products, and changes in information technology have resulted in exposure to new risks which are very different from market and credit risks. In response, the Basel Committee on Banking Supervision has developed a new regulatory framework for capital measurement and standards for the banking sector. This has formally defined operational risk and introduced corresponding capital requirements.
Many banks are undertaking quantitative modelling of operational risk using the Loss Distribution Approach (LDA) based on statistical quantification of the frequency and severity of operational risk losses. There are a number of unresolved methodological challenges in the LDA implementation. Overall, the area of quantitative operational risk is very new and different methods are under hot debate.
This book is devoted to quantitative issues in LDA. In particular, the use of Bayesian inference is the main focus. Though it is very new in this area, the Bayesian approach is well suited for modelling operational risk, as it allows for a consistent and convenient statistical framework for quantifying the uncertainties involved. It also allows for the combination of expert opinion with historical internal and external data in estimation procedures. These are critical, especially for low-frequency/high-impact operational risks.
This book is aimed at practitioners in risk management, academic researchers in financial mathematics, banking industry regulators and advanced graduate students in the area. It is a must-read for anyone who works, teaches or does research in the area of financial risk.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
Dr. Pavel V. Shevchanko is a Principal Research Scientist in the Division of Mathematics, Informatics and Statistics of CSIRO (The Commonwealth Scientific and Industrial Research Organisation of Australia). Dr Shevchenko joined CSIRO in 1999 to work in the area of financial risk. He leads research and commercial projects on the modelling of operational and credit risks; option pricing; insurance; modelling commodities and foreign exchange; and the development of relevant numerical methods and software. He received a MSc from Moscow Institute of Physics and Technology and Kapitza Institute for Physical Problems in 1994; and a PhD from The University of New South Wales in 1999 in theoretical physics. Dr Shevchenko has published extensively in academic journals, consults for major financial institutions and is a frequent presenter at industry and academic conferences.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Modelling Operational Risk Using Bayesian Inference
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Springer, 2011
ISBN 10 : 3642159222
ISBN 13 : 9783642159220
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Modelling Operational Risk Using Bayesian Inference
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ISBN 13 : 9783642159220
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Modelling Operational Risk Using Bayesian Inference
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Modelling Operational Risk Using Bayesian Inference
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Modelling Operational Risk Using Bayesian Inference (Hardcover)
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Hardcover. Etat : new. Hardcover. The management of operational risk in the banking industry has undergone explosive changes over the last decade due to substantial changes in the operational environment. Globalization, deregulation, the use of complex financial products, and changes in information technology have resulted in exposure to new risks which are very different from market and credit risks. In response, the Basel Committee on Banking Supervision has developed a new regulatory framework for capital measurement and standards for the banking sector. This has formally defined operational risk and introduced corresponding capital requirements.Many banks are undertaking quantitative modelling of operational risk using the Loss Distribution Approach (LDA) based on statistical quantification of the frequency and severity of operational risk losses. There are a number of unresolved methodological challenges in the LDA implementation. Overall, the area of quantitative operational risk is very new and different methods are under hot debate.This book is devoted to quantitative issues in LDA. In particular, the use of Bayesian inference is the main focus. Though it is very new in this area, the Bayesian approach is well suited for modelling operational risk, as it allows for a consistent and convenient statistical framework for quantifying the uncertainties involved. It also allows for the combination of expert opinion with historical internal and external data in estimation procedures. These are critical, especially for low-frequency/high-impact operational risks.This book is aimed at practitioners in risk management, academic researchers in financial mathematics, banking industry regulators and advanced graduate students in the area. It is a must-read for anyone who works, teaches or does research in the area of financial risk. This has formally defined operational risk and introduced corresponding capital requirements.Many banks are undertaking quantitative modelling of operational risk using the Loss Distribution Approach (LDA) based on statistical quantification of the frequency and severity of operational risk losses. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9783642159220
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Modelling Operational Risk Using Bayesian Inference
Edité par
Springer, 2011
ISBN 10 : 3642159222
ISBN 13 : 9783642159220
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Couverture rigide
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Modelling Operational Risk Using Bayesian Inference
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Modelling Operational Risk Using Bayesian Inference
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ISBN 10 : 3642159222
ISBN 13 : 9783642159220
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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The management of operational risk in the banking industry has undergone explosive changes over the last decade due to substantial changes in the operational environment. Globalization, deregulation, the use of complex financial products, and changes in information technology have resulted in exposure to new risks which are very different from market and credit risks. In response, the Basel Committee on Banking Supervision has developed a new regulatory framework for capital measurement and standards for the banking sector. This has formally defined operational risk and introduced corresponding capital requirements.Many banks are undertaking quantitative modelling of operational risk using the Loss Distribution Approach (LDA) based on statistical quantification of the frequency and severity of operational risk losses. There are a number of unresolved methodological challenges in the LDA implementation. Overall, the area of quantitative operational risk is very new and different methods are under hot debate.This book is devoted to quantitative issues in LDA. In particular, the use of Bayesian inference is the main focus. Though it is very new in this area, the Bayesian approach is well suited for modelling operational risk, as it allows for a consistent and convenient statistical framework for quantifying the uncertainties involved. It also allows for the combination of expert opinion with historical internal and external data in estimation procedures. These are critical, especially for low-frequency/high-impact operational risks.This book is aimed at practitioners in risk management, academic researchers in financial mathematics, banking industry regulators and advanced graduate students in the area. It is a must-read for anyone who works, teaches or does research in the area of financial risk. 320 pp. Englisch. N° de réf. du vendeur 9783642159220
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Modelling Operational Risk Using Bayesian Inference
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Springer Berlin Heidelberg, 2011
ISBN 10 : 3642159222
ISBN 13 : 9783642159220
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Gebunden. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents Bayesian framework for operational risk that can be used by banks to resolve quantitative challenges with implementation of Basel II advanced measurement approachNumerous examples will help risk practitioners to quantify operational risks. N° de réf. du vendeur 5050983
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