The present work aims at implementing a quantitative model to articulate the banking risk appetite framework (RAF). The research would like to achieve two main objectives: 1) to build up the risk appetite statement (RAS) as a static ‘picture’ of the banks’ risk profile, 2) to develop a quantitative approach with which to implement the RAF. First, the authors calculated eight indicators describing the consumption of the banks’ capital by the three risks stated in the Pillar I of the ‘Basel II’ accord. They then assumed the variables would follow a standardised Student’s t-distribution; they found out the 99th and the 95th percentile of the distribution as the risk tolerance (RT) and risk limit (RL) levels, respectively. The authors attained the second objective by analysing the variables defined in the first step with a multiple linear regression (OLS). The main outcomes confirm that the model allows for the generalising of the relation among risks, in terms of capital consumption. The work contributes by enlarging the academic literature about banking risk management; it also contributes by tackling the shortage of regulatory rules; moreover it offers external analysts and regulators a standardised technical instrument with which to analyse the banks’ risk appetite profiles.
A Quantitative Model to Articulate the Banking Risk Appetite Framework (RAF)
GERETTO, Enrico Fioravante;
2016-01-01
Abstract
The present work aims at implementing a quantitative model to articulate the banking risk appetite framework (RAF). The research would like to achieve two main objectives: 1) to build up the risk appetite statement (RAS) as a static ‘picture’ of the banks’ risk profile, 2) to develop a quantitative approach with which to implement the RAF. First, the authors calculated eight indicators describing the consumption of the banks’ capital by the three risks stated in the Pillar I of the ‘Basel II’ accord. They then assumed the variables would follow a standardised Student’s t-distribution; they found out the 99th and the 95th percentile of the distribution as the risk tolerance (RT) and risk limit (RL) levels, respectively. The authors attained the second objective by analysing the variables defined in the first step with a multiple linear regression (OLS). The main outcomes confirm that the model allows for the generalising of the relation among risks, in terms of capital consumption. The work contributes by enlarging the academic literature about banking risk management; it also contributes by tackling the shortage of regulatory rules; moreover it offers external analysts and regulators a standardised technical instrument with which to analyse the banks’ risk appetite profiles.File | Dimensione | Formato | |
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