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 At 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 suggest addition of conservatism based on maxima of historical data.
Prerequisite to any downturn computation is identification of downturn period which exhibits circumstances of an adverse nature. Banks should be able to meet their debt obligation in spite of these unforeseen variations. The current methodology to do so, hinges on the following key steps:
However, since the existing methodology is based on absolute values of EAD and LGD (which might not paint the complete picture about the respective downturn estimates), it has a number of inherent drawbacks:
To overcome these drawbacks, we propose inclusion of credit conversion factor into downturn identification process (CCF). CCF is the ratio of difference between EAD and balance at observation to the available credit (also referred as open-to-buy) as of observation point.
Exposure At Default (EAD) for an account is driven by borrower’s hunger for credit. Various exogenous factors like credit limit, historical spending patterns, current balance etc. can help in estimating this phenomenon. Out of these, available credit limit has proved to be the best indicator for predicting EAD. It’s not so much the actual credit limit, but the drawdowns on unused limit that defines the credit hunger of the borrower. The way CCF methodology can be applied for estimating EAD downturn is as follows:
The same is highlighted in the graphic below.
Loss given default for an account is equally dependent on the ability, as much as, the intention to pay. It would be safe to assume that a defaulter utilizing large amount of the available credit won’t be paying back a major chunk of his balance. Applying this concept, we can deduce a relationship between CCF and recovery rate. However, the problem of stale data cannot be mitigated using CCF directly. To overcome this, we suggest establishing a relationship between CCF and LGD. This can be used to forecast values of LGD for the workout window, thus providing recoveries for recent time period. The steps to execute are as follows:
The same is shown in the graphic below:
We believe CCF approach is more risk sensitive (and granular) as it takes into account various factors namely customer’s hunger for credit, the intention to pay, management’s response to downturn conditions to assess downturn period. Given Basel guidelines encourage banks to look for more granular techniques, as these may provide more conservative downturn estimates, we believe CCF methodologyk is more apt for downturn estimation.
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