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 ...
The article provides a short overview of methods for constructing mathematical models in the form of...
This paper provides a practical approach to construct and learn a Bayesian network model that will e...
This chapter introduces Bayesian belief and decision networks as quantitative management tools for o...
This study aims to develop a Bayesian methodology to identify, quantify and measure operational risk...
This study aims to develop a Bayesian methodology to identify, quantify and measure operational risk...
This study aims to develop a Bayesian methodology to identify, quantify and measure operational risk...
AbstractThis study aims to develop a Bayesian methodology to identify, quantify and measure operatio...
This study aims to develop a Bayesian methodology to identify, quantify and measure operational risk...
This study aims to develop a Bayesian methodology to identify, quantify and measure operational risk...
The management of operational risk in the banking industry has undergone explosive changes over the ...
AbstractThis study aims to develop a Bayesian methodology to identify, quantify and measure operatio...
The exposure of banks to operational risk is increased in the recent years. The Basel Committee on B...
The Basel Committee on Banking Supervision has released, in the last few years, recommen- dations fo...
The Basel Committee on Banking Supervision has released, in the last few years, recommen- dations fo...
This paper describes the development of a tool, based on a Bayesian network model, that provides pos...
The article provides a short overview of methods for constructing mathematical models in the form of...
This paper provides a practical approach to construct and learn a Bayesian network model that will e...
This chapter introduces Bayesian belief and decision networks as quantitative management tools for o...
This study aims to develop a Bayesian methodology to identify, quantify and measure operational risk...
This study aims to develop a Bayesian methodology to identify, quantify and measure operational risk...
This study aims to develop a Bayesian methodology to identify, quantify and measure operational risk...
AbstractThis study aims to develop a Bayesian methodology to identify, quantify and measure operatio...
This study aims to develop a Bayesian methodology to identify, quantify and measure operational risk...
This study aims to develop a Bayesian methodology to identify, quantify and measure operational risk...
The management of operational risk in the banking industry has undergone explosive changes over the ...
AbstractThis study aims to develop a Bayesian methodology to identify, quantify and measure operatio...
The exposure of banks to operational risk is increased in the recent years. The Basel Committee on B...
The Basel Committee on Banking Supervision has released, in the last few years, recommen- dations fo...
The Basel Committee on Banking Supervision has released, in the last few years, recommen- dations fo...
This paper describes the development of a tool, based on a Bayesian network model, that provides pos...
The article provides a short overview of methods for constructing mathematical models in the form of...
This paper provides a practical approach to construct and learn a Bayesian network model that will e...
This chapter introduces Bayesian belief and decision networks as quantitative management tools for o...