1 / 23

Loss distribution approach to assessing operational risk

Loss distribution approach to assessing operational risk. Ekaterina Ovchinnikova Finance Academy under the Government of the Russian Federation. State-of-the art methods and models for financial risk management September 14-17,2009. Risk Management. Identify Assess

bell-roberson
Download Presentation

Loss distribution approach to assessing operational risk

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Loss distribution approach to assessing operational risk Ekaterina Ovchinnikova FinanceAcademyundertheGovernmentoftheRussianFederation State-of-the art methods and models for financial risk management September 14-17,2009

  2. Risk Management Identify Assess Control Mitigate Quantitative assessment • BIA: • TSA: • AMA: theORC estimate is found from bank’s internal model

  3. Loss distribution approach • F(frequency)– the number of risk events occurred during a set period • S(severity)– the amount of operational loss resulting from a single event • T(total risk) – the sum ofF random variablesS, i.e. total loss over the set period • The computation is carried out simultaneously for several homogeneous groups with due account for dependencies • OR capital estimate is calculated as Value-at-Risk– 90-99.9% quantile of the aggregate loss distributionT. Modeling is based on Monte-Carlo simulation

  4. External fraud: Modeling (1) • Risk event – illegal obtention of a retail loan and/or deliberate default • The law for Severity is chosen using sample data. Data is collected via the everyday monitoring of mass media. Distribution is fittedby checking statistical hypotheses. Parameters are estimated by maximum likelihood method • Frequency follows binomial or Poisson law (this could be derived from the credit undewriting workflow). Parameters are set according to the peculiar features of the institution

  5. External fraud: Modeling (2) • Prediction horizon – 1 month, α = 99% • Amounts in terms of money are adjusted to correspond the CPI level in May 2008 (RUR) • Loss amount is considered without recovery • The calculation is carried out for an abstract credit institution * includingexpress loans, immediate needs loansand credit cards

  6. Trial pattern (1)

  7. Trial pattern (2)

  8. Results(May 2008)

  9. Results (Shortfall) • Disastrous loss estimate ES=E(T|T>VAR)exceedsVAR by2.2%

  10. Whу use EVT?

  11. What is EVT? • Block maxima method • Peaks over threshold method

  12. Internal fraud losses

  13. Choosing the threshold u ME (u)=E(S-u|S>u)~

  14. Parameter Estimation

  15. Distribution of Excesses (S-u>s|S>u) GPD fit is №1 according to K-S test (33 other distributions checked)

  16. Distribution of values below u Heavy tailed as well

  17. Challenges and Solutions

  18. The behavior of ML estimates

  19. Software • Enterprise-wide OR management: • SAS Oprisk, Oracle Reveleus, RCS OpRisk Suite, Fermat OpRisk, AlgoOpVar • Russian vendors – Ultor, Zirvan • Distribution fitting and Monte-Carlo Simulation • Palisade @Risk • MathwaveEasyFit

  20. Thank You for Your Attention!

  21. Consumer loan (EXP)

  22. Car loan (AVT)

  23. Mortgage (IPT)

More Related