| 書目名稱 | Modelling Operational Risk Using Bayesian Inference |
| 編輯 | Pavel V. Shevchenko |
| 視頻video | http://file.papertrans.cn/637/636520/636520.mp4 |
| 概述 | Presents Bayesian framework for operational risk that can be used by banks to resolve quantitative challenges with implementation of Basel II advanced measurement approach.Numerous examples will help |
| 圖書封面 |  |
| 描述 | .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 i |
| 出版日期 | Book 2011 |
| 關(guān)鍵詞 | Bayesian inference; Loss distribution approach; Markov chain Monte Carlo; Operational Risk; Quantitative |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-642-15923-7 |
| isbn_softcover | 978-3-642-42353-6 |
| isbn_ebook | 978-3-642-15923-7 |
| copyright | Springer-Verlag Berlin Heidelberg 2011 |
|Archiver|手機(jī)版|小黑屋|
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