Quantification of Operational Risk: Statistical Insights on Coherent Risk Measures

22 Pages Posted: 6 Jul 2019

See all articles by Dany Ng Cheong Vee

Dany Ng Cheong Vee

Bank of Mauritius

Preethee Gonpot

University of Mauritius

T. V. Ramanathan

Savitribai Phule Pune University (SPPU)

Date Written: June 28, 2019

Abstract

Operational risk is becoming a major part of corporate governance in companies, especially in the financial services industry. In this paper, we review some of the existing methods used to quantify operational risks in the banking and insurance industries. These methods use recent statistical concepts such as extreme value theory and copula modeling. We explore the possibility of using a coherent risk measure – expected shortfall (ES) – to quantify operational risk. The suitability of the suggested risk measures has been investigated with the help of simulated data sets for two business lines. The generalized Pareto distribution is used for modeling the tails, and three distributions – lognormal, Weibull and Gamma – are used for the body data. Our results show that ES under all three distributions tends to be significantly larger than value-at-risk, which may lead to overestimating the operational loss and consequently overestimating the capital charge. However, the modified ES seems to provide a better way of mitigating any overestimation.

Keywords: operational risk, coherent risk measures, extreme value theory (EVT), loss distribution, value-at-risk (VaR), modified expected shortfall (MES).

Suggested Citation

Ng Cheong Vee, Dany and Gonpot, Preethee and Ramanathan, T. V., Quantification of Operational Risk: Statistical Insights on Coherent Risk Measures (June 28, 2019). Journal of Operational Risk, Vol. 14, No. 2, 2019. Available at SSRN: https://ssrn.com/abstract=3414234

Dany Ng Cheong Vee (Contact Author)

Bank of Mauritius ( email )

P.O. Box 29
Port Louis
Mauritania

Preethee Gonpot

University of Mauritius ( email )

Reduit, 80837
Mauritius

T. V. Ramanathan

Savitribai Phule Pune University (SPPU) ( email )

Ganeshkhind
Ganeshkhind
Pune, Maharashtra 411007
India

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
3
Abstract Views
137
PlumX Metrics