TY - JOUR
T1 - An analysis on operational risk in international banking
T2 - A Bayesian approach (2007-2011)
AU - Martínez-Sánchez, José Francisco
AU - Martínez-Palacios, María Teresa V.
AU - Venegas-Martínez, Francisco
N1 - Publisher Copyright:
© 2016 Universidad ICESI. Published by Elsevier España, S.L.U. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
PY - 2016
Y1 - 2016
N2 - This study aims to develop a Bayesian methodology to identify, quantify and measure operational risk in several business lines of commercial banking. To do this, a Bayesian network (BN) model is designed with prior and subsequent distributions to estimate the frequency and severity. Regarding the subsequent distributions, an inference procedure for the maximum expected loss, for a period of 20 days, is carried out by using the Monte Carlo simulation method. The business lines analyzed are marketing and sales, retail banking and private banking, which all together accounted for 88.5% of the losses in 2011. Data was obtained for the period 2007-2011 from the Riskdata Operational Exchange Association (ORX), and external data was provided from qualified experts to complete the missing records or to improve its poor quality.
AB - This study aims to develop a Bayesian methodology to identify, quantify and measure operational risk in several business lines of commercial banking. To do this, a Bayesian network (BN) model is designed with prior and subsequent distributions to estimate the frequency and severity. Regarding the subsequent distributions, an inference procedure for the maximum expected loss, for a period of 20 days, is carried out by using the Monte Carlo simulation method. The business lines analyzed are marketing and sales, retail banking and private banking, which all together accounted for 88.5% of the losses in 2011. Data was obtained for the period 2007-2011 from the Riskdata Operational Exchange Association (ORX), and external data was provided from qualified experts to complete the missing records or to improve its poor quality.
KW - Bayesian analysis
KW - Monte Carlo simulation
KW - Operational risk
UR - http://www.scopus.com/inward/record.url?scp=85065060271&partnerID=8YFLogxK
U2 - 10.1016/j.estger.2016.06.004
DO - 10.1016/j.estger.2016.06.004
M3 - Artículo de revisión
AN - SCOPUS:85065060271
SN - 0123-5923
VL - 32
SP - 208
EP - 220
JO - Estudios Gerenciales
JF - Estudios Gerenciales
IS - 140
ER -