Abstract
This chapter aims to provide an overview of the main components of an operational risk measurement framework developed by financial intermediaries for which operational risk is more important. This methodology integrates a historical analysis with a scenario analysis. This chapter describes the loss data collection, the assumption and the statistical tools used in the implemented approach. It also describes the methods used to integrate the Expected Lossess (EL) and the Unexpected Lossess (UL) resulting from the two different analyses.
This chapter was prepared jointly by the authors.
The original version of this chapter was revised: Belated corrections from author have been incorporated. The erratum to this chapter is available at https://doi.org/10.1007/978-3-319-69410-8_6
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Agostini, A., Talamo, P., & Vecchione, V. (2010). Combining operational loss data with expert opinions through advanced credibility theory. The Journal of Operational Risk, 5, 3–28.
Andreatta, C., & Mazza, D. (2013). Alternative approaches to generalized Pareto distribution shape parameter estimation through expert opinions. The Journal of Operational Risk, 8(2), 47–72.
Bank of Italy. (2013). Circolare 285/2013. Disposizioni di vigilanza per le banche. https://www.bancaditalia.it/compiti/vigilanza/normativa/archivio-norme/circolari/c285/index.html.
BCBS. (2006). International convergence of capital measurement and capital standards: A revised framework. Comprehensive version, Basel Committee on Banking Supervision. http://www.bis.org/publ/bcbs128.htm.
Chernobai, A. S., Rachev, S. T., & Fabozzi, F. J. (2008). Operational risk: A guide to Basel II capital requirements, models, and analysis. Hoboken, NJ: Wiley.
Cruz, M. G. (2002). Modeling, measuring and hedging operational risk. Hoboken, NJ: Wiley.
Cruz, M. G. (2004). Operational risk modelling and analysis: Theory and practice. London: Risk Books.
European Parliament and Council. (2013a). Directive 2013/36/EU on access to the activity of credit institutions and the prudential supervision of credit institutions and investment firms, amending Directive 2002/87/EC. http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32013L0036&from=IT.
European Parliament and Council. (2013b). Regulation (EU) No. 575/2013 on prudential requirements for credit institutions and investment firms and amending Regulation (EU) No. 648/2012. http://eur-lex.europa.eu/legal-content/IT/TXT/?uri=celex%3A32013R0575.
CEBS. (2010). CEBS guidelines on stress testing (GL32). Committee of European Banking Supervisors. https://www.eba.europa.eu/documents/10180/16094/ST_Guidelines.pdf.
EBA. (2014). Guidelines on common procedures and methodologies for the supervisory review and evaluation process. European Banking Authority. https://www.eba.europa.eu/-/eba-publishes-final-guidelines-on-srep-methodologies-and-processes.
EBA. (2015). RTS on AMA assessment. European Banking Authority. https://www.eba.europa.eu/documents/10180/1100516/EBA-RTS-2015-02+RTS+on+AMA+assesment.pdf.
EBA. (2016). Guidelines on ICAAP and ILAAP information collected for SREP purposes. European Banking Authority. https://www.eba.europa.eu/-/eba-publishes-final-guidelines-on-icaap-and-ilaap-information.
Giudici, P., & Bilotta, A. (2004). Modelling operational losses: A Bayesian approach. Quality and Reliability Engineering International, 20(5), 407–417.
Ergashev, B. A. (2012). A theoretical framework for incorporating scenarios into operational risk modeling. Journal of Financial Services Research, 41, 45–161.
Embrechts, P., Klüppelberg, C., & Mikosch, T. (1997). Modelling extremal events. Applications of Mathematics, 33.
Frachot, A., Moudoulaud, O., & Roncalli, T. (2004). Loss distribution approach in practice. In The Basel handbook: A guide for financial practitioners (pp. 369–396). London: Risk Books.
Giudici, P., & Stinco, G. (2004). Modelli avanzati sui rischi operativi: l’approccio delle reti bayesiane. Bancaria, 12, 55–62.
King, J. L. (2001). Operational risk: Measurements and modelling. Hoboken, NJ: Wiley.
Klugman, S. A., Panjer, H. H., & Willmot, G. E. (2008). Loss models: From data to decisions. Hoboken, NJ: Wiley.
Mignola, G., & Ugoccioni, R. (2007). Statistical approach to operational risk management. Turin, Italy: Sanpaolo IMI Group.
Moscadelli, M. (2004). The modelling of operational risk: Experience with the analysis of the data collected by the Basel Committee. Bank of Italy, Economic Research and International Relations Area, No. 517.
Shevchenko, P. V. (2011). Modelling operational risk using Bayesian inference. Berlin: Springer.
Shevchenko, P. V., & Peters, G. W. (2013). Loss distribution approach for operational risk capital modelling under Basel II: Combining different data sources for risk estimation. arXiv preprint arXiv: 1306.1882.
Vellella, M. (1999). Valorizzazione del capitale economico e rischio operativo nella banca. Banche e Banchieri, No. 6.
Vellella, M. (2004). Le problematiche di gestione del rischio operativo. In Leone (2004), Capital Management. CEDAM.
Vellella, M. (2005). La gestione dei rischi operativi nelle banche: alcuni spunti di riflessione sulle scelte organizzative. Banche e Banchieri, No. 3.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 The Author(s)
About this chapter
Cite this chapter
Matarazzo, V., Vellella, M. (2018). Integrated Risk Measurement Approach: A Case Study. In: Leone, P., Porretta, P., Vellella, M. (eds) Measuring and Managing Operational Risk. Palgrave Macmillan Studies in Banking and Financial Institutions. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-69410-8_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-69410-8_4
Published:
Publisher Name: Palgrave Macmillan, Cham
Print ISBN: 978-3-319-69409-2
Online ISBN: 978-3-319-69410-8
eBook Packages: Economics and FinanceEconomics and Finance (R0)