Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-23T06:58:55.608Z Has data issue: false hasContentIssue false

An analysis of the feasibility of an extreme operational risk pool for banks

Published online by Cambridge University Press:  06 August 2018

Yifei Li
Affiliation:
Sydney Business School, University of Wollongong, Macquarie Place, Sydney 2000, Australia
Neil Allan
Affiliation:
Systems Centre, University of Bristol, Queen’s Building, University Walk, Clifton, BR8 1TR, UK
John Evans*
Affiliation:
Centre for Analysis of Complex Financial Systems, Summer Hill, NSW 2130, Australia
*
*Correspondence to: John Evans, Centre for Analysis of Complex Financial Systems, PO Box 363, Summer Hill, NSW 2130, Australia. Tel: +61414643658. E-mail: [email protected]

Abstract

Operational risk events in banks include extreme events with significant losses being incurred and with substantial impact on share prices. A pooling arrangement between banks that would be able to reduce overall costs and reduce share price impacts would seem desirable, but one of the major inhibiting factors to establish the feasibility of such a pooling arrangement is that statistical models of these extreme events are difficult to build with any reliability. This paper uses both quantitative and qualitative analysis of operational risk losses for EU and US banks over the period 2008–2014 to establish the feasibility of creating a pooling arrangement between the banks and concludes that such an arrangement might be feasible but would require compulsory membership of the pool and capping of losses.

Type
Paper
Copyright
© Institute and Faculty of Actuaries 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Allan, N. & Corrigan, J. (2013). Emerging Risk Assessment-Latest Practice & Innovation. Paper Presented at the Actuaries Summit, Sydney, May.Google Scholar
Battiston, S., Farmer, J.D., Flache, A., Garlaschelli, D., Haldane, A.G., Heesterbeek, H. & Scheffer, M. (2016). Complexity theory and financial regulation. Science, 351(6275), 818819.Google Scholar
Bavelas, A. (1950). Communication patterns in task-oriented groups. Journal of the Acoustical Society of America, 22(6), 725730.Google Scholar
Chavez-Demoulin, V., Embrechts, P. & Hofert, M. (2016). An extreme value approach for modeling operational risk losses depending on covariates. Journal of Risk & Insurance, 83(3), 735776.Google Scholar
Chernobai, A., Jorion, P. & Yu, F. (2011). The determinants of operational risk in U.S. financial institutions. Journal of Financial & Quantitative Analysis, 46(6), 16831725.Google Scholar
Daníelsson, J. (2008). Blame the models. Journal of Financial Stability, 4(4), 321328.Google Scholar
Evans, J., Allan, N. & Cantle, N. (2017). A cladistics insight into the management of the world economic forum global risks. Economic Papers, 36(2), 185198.Google Scholar
Fiordelisi, F., Soana, M.-G. & Schwizer, P. (2014). Reputational losses and operational risk in banking. The European Journal of Finance, 20(2), 105124.Google Scholar
Ganegoda, A. & Evans, J. (2013). A scaling model for severity of operational losses using Generalized Additive Models for Location Scale and Shape (GAMLSS). Annals of Actuarial Science, 7(1), 61100.Google Scholar
Haldane, A.G. & May, R.M. (2011). Systemic risk in banking ecosystems. Nature, 469(7330), 351355.Google Scholar
Joseph, A. & Chen, G. (2014). Cross-border portfolio investment networks and indicators for financial crises. Available online at the address http://arxiv.org/abs/1306.0215 [accessed 13-Nov-2016].Google Scholar
Leavitt, H.J. (1951). Some effects of certain communication patterns on group performance. Journal of Abnormal and Social Psychology, 46(1), 3850.Google Scholar
Li, Y., Allan, N. & Evans, J. (2017). An analysis of operational risk events in US and European Banks 2008–2014. Annals of Actuarial Science, 11(2), 315342.Google Scholar
Matthews, L.J., Edmonds, J., Wildman, W.J. & Nunn, C.L. (2013). Cultural inheritance or cultural diffusion of religious violence? A quantitative case study of the radical reformation. Religion, Brain & Behavior, 3(1), 315.Google Scholar
Mitleton-Kelly, E. (2003). Complex systems and evolutionary perspectives on organisations: the application of complexity theory to organisations. In E. Mitleton-Kelly, Ed. Complex Systems and Evolutionary Perspectives on Organisations (pp. 21221). Elsevier Science, Oxford.Google Scholar
Mittnik, S., Paterlini, S. & Yener, T. (2013). Operational risk dependencies and the determination of risk capital. Journal of Operational Risk, 8(4), 83104.Google Scholar
Sturm, P. (2013). Operational and reputational risk in the European banking industry: the market reaction to operational risk events. Journal of Economic Behaviour and Organization, 85(2013), 191206.Google Scholar