Economic capital for credit risk can be defined as the level of capital required to cover the economic effects of risk-taking credit transactions at portfolio level. Economic capital can be analysed and used at various levels – ranging from firm-wide aggregation, to risk-type or business-line level, and down further still to the individual portfolio or exposure level. Economic capital modelling has been driven by both internal capital management needs of banks and regulatory guidelines. In particular, Pillar 2 (Supervisory Review Process and ICAAP) of the Basel II Framework may involve an assessment of a banks’ economic capital framework.
Asymmetrix Economic Capital Software implements a Monte Carlo Simulation based Multi-factor Model that allows economic capital estimation to ensure that the banks is adequately capitalized to absorb tail-risks. Integrated Capital allocation algorithm allows attributing economic capital at various levels - ranging from portfolio-level, to business-line level, and down further individual customers and exposure level.
Asymmetrix Economic Capital Model is a generalisation of Basel II IRB ASRF model, extending the model to incorporate multiple risk factors such as country risk, industry risk and borrower risk. The software calculates portfolio level Credit VaR (at user defined confidence level), Expected Loss, Conditional VaR/Expected Shortfall (average of losses beyond VaR) and economic capital requirement using Monte Carlo Simulation of likely states of risk factors and individual borrower's asset values.
The software allocates economic capital to individual customers, which can then be aggregated at any desired level. For instance, economic capital consumption by an individual industry, business line, customer type etc. can be analysed.
The software calculates the capital requirement for borrower and sector concentration risk required under Basel II Pillar-II guidelines.
An integrated dashboard provides standard reports required at facility, borrower and portfolio level.
The software allows dependence between risk factors to be modelled using a correlation matrix and user-selected copula (Gaussian Copula or t-copula).