Advertisement

Empirical Examination of Operational Loss Distributions

  • Svetlozar T. Rachev
  • Anna Chernobai
  • Christian Menn

Abstract

Until very recently, it has been believed that banks are exposed to two main types of risks: credit risk (the counterparty failure risk) and market risk (the risk of loss due to changes in market indicators, such as interest rates and exchange rates), in the order of importance. The remaining financial risks have been put in the category of other risks, operational risk being one of them. Recent developments in the financial industry have shown that the importance of operational risk has been largely under-estimated. Newly defined capital requirements set by the Basel Committee for Banking Supervision in 2004, require financial institutions to estimate the capital charge to cover their operational losses [6].

Keywords

Operational Risk Operational Loss Loss Distribution Banking Supervision Homogeneous Poisson Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Bening VE, Korolev VY (2002) Generalized Poisson Models and their Applications in Insurance and Finance. VSP International Science Publishers, Utrecht, BostonGoogle Scholar
  2. [2]
    BIS (1998) Operational risk management. http://www.bis.orgGoogle Scholar
  3. [3]
    BIS (2001) Consultative document: operational risk. http://www.bis. orgGoogle Scholar
  4. [4]
    BIS (2001) Working paper on the regulatory treatment of operational risk. http://www.bis.orgGoogle Scholar
  5. [5]
    BIS (2003) The 2002 loss data collection exercise for operational risk: summary of the data collected. http://www.bis.orgGoogle Scholar
  6. [6]
    BIS (2004) International convergence of capital measurement and capital standards. http://www.bis.orgGoogle Scholar
  7. [7]
    Chernobai A, Menn C, Rachev ST, Trück S (2005) Estimation of operational value-at-risk in the presence of minimum collection thresholds. Department of Statistics and Applied Probability, University of California Santa BarbaraGoogle Scholar
  8. [8]
    Chernobai A, Menn C, Trück S, Rachev S (2005) A note on the estimation of the frequency and severity distribution of operational losses. Mathematical Scientist 30(2)Google Scholar
  9. [9]
    Chernobai A, Rachev S, Fabozzi F (2005) Composite goodness-of-fit tests for left-truncated loss samples. Department of Statistics and Applied Probability, University of California Santa BarbaraGoogle Scholar
  10. [10]
    Embrechts P, Klüppelberg C, Mikosch T (1997) Modeling Extremal Events for Insurance and Finance. Springer-Verlag, BerlinGoogle Scholar
  11. [11]
    Jorion P (2000) Value-at-Risk: The New Benchmark for Managing Financial Risk, Second edition. McGraw-Hill, New YorkGoogle Scholar
  12. [12]
    Moscadelli M, Chernobai A, Rachev ST (2005) Treatment of missing data in the field of operational risk: Effects on parameter estimates, EL, UL and CVaR measures. Operational Risk, June 2005Google Scholar
  13. [13]
    Rachev ST, Mittnik S (2000) Stable Paretian Models in Finance. John Wiley & Sons, New YorkGoogle Scholar
  14. [14]
    Samorodnitsky G, Taqqu MS (1994) Stable Non-Gaussian Random Processes. Stochastic Models with Infinite Variance. Chapman & Hall, LondonGoogle Scholar
  15. [15]
    Zolotarev VM (1986) One-dimensional stable distributions. Translations of Mathematical Monographs, vol. 65. American Mathematical Society, ProvidenceGoogle Scholar

Copyright information

© Deutscher Universitäts-Verlag/GWV Fachverlage GmbH, Wiesbaden 2006

Authors and Affiliations

  • Svetlozar T. Rachev
    • 1
  • Anna Chernobai
    • 2
  • Christian Menn
    • 3
  1. 1.Institut für Statistik und Mathematische WirtschaftstheorieUniversität KarlsruheKarlsruhe
  2. 2.Department of Statistics and Applied ProbabilityUniversity of CaliorniaSanta BarbaraUSA
  3. 3.School of Operations Research and Industrial EngineeringCornell UniversityIthacaUSA

Personalised recommendations