Asymptotic results for over-dispersed operational risk by using the asymptotic expansion method
- 63 Downloads
In this paper, the author considers a new Loss-distribution-approach model, in which the over-dispersed operational risks are modeled by the compound negative binomial process. In the single dimensional case, asymptotic expansion for the quantile of compound negative binomial process is explored for computing the capital charge of a bank for operational risk. Moreover, when the dependence structure between different risk cells is modeled by the Frank copula, this approach is extended to the multi-dimensional setting. A practical example is given to demonstrate the effectiveness of approximation results.
KeywordsAsymptotic expansion multivariate dependence operational risk over-dispersed value at risk
Unable to display preview. Download preview PDF.
- Basel Committee on Banking Supervision, Basell II: The new Basel Capital Accord, Second consultative paper, Report to the Bank for International Settlements, URL: http://www.bis.org, 2001.
- Basel Committee on Banking Supervision. Basell II: The new Basel Capital Accord, Third consultative paper, Report to the Bank for International Settlements, URL: http://www.bis.org, 2003.
- Nash R, The Three Pillars of Operational Risk, Operational Risk: Regulation, Analysis, and Management, Financial Times Prentice Hall, London, 2003.Google Scholar
- Alexander C, Statistical models of operational losses, in Operational Risk: Regulation, Analysis, and Management (ed. by Alexander C), Financial Times Prentice Hall, London, 2003, 129–168.Google Scholar
- Anders U, The path to operational risk economic capital, in Operational Risk: Regulation, Analysis, and Management (ed. by Alexander C), Financial Times Prentice Hall, London, 2003, 215–225.Google Scholar
- Haubenstock M and Hardin L, The loss distribution approach, in Operational Risk: Regulation, Analysis, and Management (ed. by Alexander C), Financial Times Prentice Hall, London, 2003, 171–192.Google Scholar
- Frachot A, Georges P, and Roncalli T, Loss distribution approach in practice, Groupe de Recherche Opérationnelle, Crédit Lyonnais, Working Paper, URL: http://gro.creditlyonnais.fr/content/wp/lda-practice.pdf, 2003.Google Scholar
- Embrechts P, Furrer H, and Kaufmann R, Quantifying regulatory capital for operational risk, RiskLab, ETH Zürich, Working Paper, 2003.Google Scholar
- Bachelier S, Operational risk: Some practical questions, RiskLab-madrid, Universidad Autonoma de Madrid, URL: http://www.crest.fr/ckfinder/userfiles/files/pageperso/relie, 2007.Google Scholar
- Havlický J, Quantification of operational risk loss within the loss distributional approach, Euro Working Group on Financial Modeling, Stockholm, 15–17 May, 2008.Google Scholar
- Panjer H H, Operational Risk: Modeling Analytics (I), 1st edition, John Wiley & Sons, 2006.Google Scholar
- Cope E and Antonini G, Observed correlations and dependencies among operational losses in the ORX consortium database, The Journal of Operational Risk, 2008, 3: 47–74.Google Scholar
- Böcker K and Klüppelberg C, Modeling and measuring multivariate operational risk with Lévy copulas, Journal of Operational Risk, 2008, 3(2): 3–27.Google Scholar
- Cont R and Tankov P, Financial Modeling with Jump Processes, Chapman & Hall, CRC: Boca Raton, 2004.Google Scholar
- Dionne G and Dahen H, What about under-evaluating operational value at risk in the banking sector? The Series Working Papers of the Canada Research Chair in Risk Management, Cahier de recherche/Working Paper, URL: http://neumann.hec.ca/gestiondesrisques/07-05.pdf, 07–23, September 2007.Google Scholar
- Schmock U, Modeling dependent credit risks with extensions to CreditRisk+ and application to operational risk, Lecture Notes, URL: http://www.fam.tuwien.ac.at/schmock/notes/ExtentionsCreditRiskPlus.pdf, December 2008.Google Scholar