rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Modelling Operational Risk Using Bayesian Inference
  name:
    name part: Shevchenko, Pavel V.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2011
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVII, 302 p. online resource.
  abstract: 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.
  table of contents: Operational 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.
  note: by Pavel V. Shevchenko.
  subject:
    topic: Statistics
  subject:
    topic: Finance
  subject:
    topic: Probabilities
  subject:
    topic: Statistics
  subject:
    topic: Statistics for Business/Economics/Mathematical Finance/Insurance
  subject:
    topic: Statistical Theory and Methods
  subject:
    topic: Probability Theory and Stochastic Processes
  subject:
    topic: Finance, general
  classification: QA276-280
  classification: 330.015195
  related item:
    title info:
      title: Springer eBooks
  identifier: 9783642159237
  identifier: http://dx.doi.org/10.1007/978-3-642-15923-7
  record info:
    record creation date: 110118
    record change date: 20151204161430.0
    record identifier: 978-3-642-15923-7