000 03831nam a22004935i 4500
001 978-3-642-15923-7
003 DE-He213
005 20140220083748.0
007 cr nn 008mamaa
008 110118s2011 gw | s |||| 0|eng d
020 _a9783642159237
_9978-3-642-15923-7
024 7 _a10.1007/978-3-642-15923-7
_2doi
050 4 _aQA276-280
072 7 _aPBT
_2bicssc
072 7 _aK
_2bicssc
072 7 _aBUS061000
_2bisacsh
082 0 4 _a330.015195
_223
100 1 _aShevchenko, Pavel V.
_eauthor.
245 1 0 _aModelling Operational Risk Using Bayesian Inference
_h[electronic resource] /
_cby Pavel V. Shevchenko.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2011.
300 _aXVII, 302p. 25 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aOperational Risk and Basel II -- Loss Distribution Approach -- Calculation of Compound Distribution -- Bayesian approach for LDA -- Addressing the Data Truncation Problem -- Modelling Large Losses -- Modelling Dependence -- List of Distributions -- Selected Simulation Algorithms -- Solutions for Selected Problems -- References -- Index.
520 _aThe 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.
650 0 _aStatistics.
650 0 _aDistribution (Probability theory).
650 0 _aMathematical statistics.
650 0 _aEconomics
_xStatistics.
650 0 _aBanks and banking.
650 1 4 _aStatistics.
650 2 4 _aStatistics for Business/Economics/Mathematical Finance/Insurance.
650 2 4 _aStatistical Theory and Methods.
650 2 4 _aProbability Theory and Stochastic Processes.
650 2 4 _aFinance /Banking.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642159220
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-15923-7
912 _aZDB-2-SMA
999 _c107098
_d107098