As part of Basel accords, banks and other financial institution should hold sufficient capital to buffer against large unexpected losses. Minimum capital required, for pillar 1, is a combination of credit, operations and market risk.
While the Probability of Default (PD) is conditioned to include defaults to a 99.9 percentile value, no such amendments were made to Exposure Given Default (EAD) or the Loss Given Default (LGD) to reflect such severe scenarios. Regulatory guidance was issued to condition EAD and LGD values to observed period of downturn within the data. Regulatory guidelines suggested addition of conservatism based on maxima of historical data. However, the existing downturn methodology through observed maxima of default rates lacks the ability to capture drivers of EAD and LGD. It can thus lead to suboptimal downturn estimation.
This paper highlights the challenges of current downturn methodology and proposes a novel approach of including Credit Conversion Factor (CCF) into the downturn identification process. The paper elaborates how CCF approach takes into account various factors namely customer’s hunger for credit, the intention to pay, management’s response to downturn conditions etc. to identify downturn period.