Open Access Journal Article

Reasonableness and Correctness for Operational Value-at-Risk

by Peter Mitic a, b,* orcid
a
Department of Computer Science, University College London, London, UK
b
Risk Analytics, Santander Bank, London, UK
*
Author to whom correspondence should be addressed.
EAL  2023 2(3):31; https://doi.org/10.58567/eal02030005
Received: 17 May 2023 / Accepted: 12 June 2023 / Published Online: 24 June 2023
View Full-Text
Download PDF

Abstract

Calculating the amount of regulatory capital to cover unexpected losses due to operational events in the upcoming year has caused problems because of difficulties in fitting probability distributions to data. It is consequently difficult to judge an appropriate level of capital that reflects the risk profile of a financial institution. We provide theoretical and empirical analyses to link the calculated capital to the sum of losses using appropriate statistical approximations. We conclude that, in order to reasonably reflect the associated risk, the capital should be approximately half the sum of losses, with a wide bound for the ratio of capital to sum.

Keywords: Value at Risk; Central Limit Theorem; Loss Distribution; Loss Sum; Operational Risk; Regulatory Capital;
Copyright: © 2023 by Mitic. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Show Figures

Share and Cite

ACS Style
Mitic, P. Reasonableness and Correctness for Operational Value-at-Risk. Economic Analysis Letters, 2023, 2, 31. https://doi.org/10.58567/eal02030005
AMA Style
Mitic P. Reasonableness and Correctness for Operational Value-at-Risk. Economic Analysis Letters; 2023, 2(3):31. https://doi.org/10.58567/eal02030005
Chicago/Turabian Style
Mitic, Peter 2023. "Reasonableness and Correctness for Operational Value-at-Risk" Economic Analysis Letters 2, no.3:31. https://doi.org/10.58567/eal02030005
APA style
Mitic, P. (2023). Reasonableness and Correctness for Operational Value-at-Risk. Economic Analysis Letters, 2(3), 31. https://doi.org/10.58567/eal02030005

Article Metrics

Article Access Statistics

References

  1. Abdymomunov, A. and Curti, F. (2020). Quantifying and stress testing operational risk with peer banks’ data. Journal of Financial Services Research, 57, 287-313. https://doi.org/10.1007/s10693-019-00320-w
  2. Abdymomunov, A., Curti, F., and Mihov, A. (2020). U.S. banking sector operational losses and the macroeconomic environment. Journal of Money, Credit, and Banking, 52(1), 115–144. https://doi.org/10.1111/jmcb.12661
  3. Berkowitz, J. and O’Brian, J. (2002). How accurate are value-at-risk models at commercial banks? Journal of Finance, 57(3), 1093–1111. https://doi.org/10.1111/1540-6261.00455
  4. BIS (2006). Bank for International Settlements BCBS128 - International Convergence of Capital Measurement and Capital Standards. https://www.bis.org/publ/bcbs128.htm
  5. Chen,Q. and Wen,Y. (2010). A BP-Neural Network Predictor Model for Operational Risk Losses of Commercial Bank. 3rd Int. Symposium on Information Processing, Qingdao, 291-295. https://doi.org/10.1109/ISIP.2010.43
  6. Efron, B and Tibshirani, R.J. (1994) An Introduction to the Bootstrap, Chapman Hall
  7. Frachot, A., Georges, P., and Roncalli, T. (2001). Loss distribution approach for operational risk. Working paper, Groupe de Recherche Operationnelle, Credit Lyonnais, France. http://ssrn.com/abstract=1032523
  8. Mitic, P., Cooper, J. and Bloxham, N. (2020). Incremental Value-at-Risk. Journal of Model Risk Validation, 14(1), 1-37. https://doi.org/10.21314/JRMV.2020.216
  9. Mitic, P. (2015). Improved Goodness-of-Fit tests for Operational Risk. Journal of Operational Risk, 15(1), 77-126. https://doi.org/10.21314/JOP.2015.159
  10. Mitic, P. (2023). Credible Value-at-Risk. Submitted to the Journal of Operational Risk
  11. Pena,A., Patino, A., Chiclana, F., Caraffini, F. and Congora, M. (2021). Fuzzy convolutional deep-learning model to estimate the operational risk capital using multi-source risk events. Applied Soft Computing 107(107381). https://www.sciencedirect.com/science/article/pii/S1568494621003045
  12. Stuart, R. (2023) Why did Credit Suisse fail? Economics Observatory https://www.economicsobservatory.com/
  13. Weisstein, E. W. (2023) Wolfram MathWorld. https://mathworld.wolfram.com