Exposure At Default

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What Is Exposure At Default (EAD)?

Exposure at default (EAD), is a loss prediction or calculation made by the bank in the scenario of default payment by the debtor. The primary purpose of EAD is to predict the loss the lender will suffer due to non-payment of debt.

Exposure At Default

EAD acts as a metric for understanding the potential defaulters of all debtors. Plus, it helps in risk assessment arising from such loans. Also, exposure at default determines the credit limit for borrowers. However, incorrect data can cause wrong calculations for actual losses. Moreover, it helps financial institutions better understand and manage the risks associated with their lending activities.

  • Exposure at Default, or EAD, is a form of estimation made by the lenders to determine the maximum losses they could incur if the borrowers default on the loan taken. 
  • There are two approaches to this: the advanced internal ratings-based (A-IRB) approach and the foundation internal ratings-based (F-IRB) approach.
  • The formula for EAD includes the expected loss (EL) to the product of PD (probability of default) and LGD (Loss given default). 
  • EAD is an essential component of credit risk assessment because it helps financial institutions estimate the potential losses they might face in the event of default.

Exposure At Default (EAD) Explained

Exposure at default is an effective method for calculating the future losses a chance to incur if the borrowers fail to repay the loan amount. It is expressed either as a dollar or a percentage. Hence, in simple terms, EAD quantifies how much of a loan or credit facility is outstanding at the time of default. It is a vital element of credit risk management. EAD helps lenders understand the default risk they are exposed to.

Moreover, these take into account not just the current balance of the loan but also any potential future increases in the outstanding amount. Thus, it can include factors such as unused credit lines that the borrower has access to but hasn't utilized, as well as any collateral or security that might be available to the lender in case of default.

Typically, such setups are seen in financial institutions like banks that provide customer loans. In these cases, there is a constant risk of debt failure by the latter. And if it turns out to be accurate, it can bring huge losses to the lender. Therefore, to save from future consequences, the EAD value is calculated. However, it differs from bad debts. Although similar, bad debts refer to unrecoverable customer losses during a trade. In contrast, the exposure at debt calculation tries to predict loan defaulters. Additionally, it plays a vital role in determining the amount of regulatory capital required to cover potential losses, as well as in pricing loans and setting lending terms.

It's important to note that exposure at default models is part of a broader suite of credit risk management tools, including probability of default (PD) models and loss-given default (LGD) models. Together, these components provide a comprehensive view of lending activities' potential risks and losses.

How To Calculate?

Exposure at default calculation (EAD) involves estimating the potential exposure of a credit facility at the time of default. Two approaches help in determining the EAD.

1. F-IRB Approach

The foundation internal ratings-based approach (or F-IRB) is a method used to calculate EAD by considering forward valuations and commitment details. Here, banks use the probability of default to determine the risk exposure. However, it might avoid any security or collateral used in the arrangement. Hence, this approach requires robust data and sophisticated modeling.

2. A-IRB Approach

Advanced internal ratings-based approach (A-IRB) intends to expose their level of risk and mitigate it. As a result, it is widely used by various financial institutions like banks. They conduct an analysis based on the borrower's character and ability to repay. Here, banks use PD, LGD, and EAD to get an overview of credit risk during unknown circumstances. These models provide a more accurate and risk-sensitive estimation of EAD.

Formula

The formula for calculating Exposure at Default (EAD) is as follows:

Exposure At default = Expected Loss (EL)/PD x LGD

Where:

  • The expected loss is the loss assumed by the lender.
  • At the same time, the probability of default is a method of expected loss calculation by big corporations.
  • LGD refers to the share of the asset lost if the borrower defaults.

Therefore, the formula provides a straightforward way to estimate the total exposure a lender might face if a borrower defaults on their credit obligations, thus considering the current financial situation and future drawdowns.

Examples

Let us look at the examples of exposure at default to comprehend the concept better:

Example #1

Assuming that the Trust Bank in the US has a credit portfolio with the following characteristics:

Probability of Default (PD): 0.08 (8%)

Loss Given Default (LGD): 0.50 (50%)

Expected Loss (EL): $400,000

Using these values, we can calculate the Exposure at Default (EAD) for the bank's credit portfolio:

EAD = EL / (PD x LGD)

 = $400,000 / (0.08 x 0.50)

= $400,000 / 0.04

Exposure At Default = $10,000,000

In this example, the Exposure at default (EAD) for the bank's credit portfolio is calculated to be $10,000,000. Hence, under the given assumptions, the bank's total exposure to potential losses in the event of default across its credit portfolio is $10,000,000.

Example #2

According to Fitch's most recent Monthly U.S. CLO Index, U.S. broadly syndicated loan CLO under its watch maintained adequate rating buffers in the first quarter of 2023, despite some mildly degrading as default risk increased.  Hence, older transactions were less healthy than new transactions that entered monitoring in 1Q23. Moreover,  CCC+' to 'C' vulnerability had an average of 3.0%, while senior and junior over-collateralization (OC) test cushions had averages of 10.5% and 5.3%, respectively.

During the quarter, rising defaults put pressure on rating reserves generally. Three issuers left the status during 1Q23, and 12 new defaulted issuers entered it. By the end of March 2023, there were 19 delinquent issuers overall, up from 10 in December 2022. Therefore, as per the report, the average EAD in CLOs (Collateralized Loan Obligations) increased from 0.4% to 0.7% from December 2022.

Exposure At Default vs Loss Given Default

Although EAD and loss given default (LGD) are closely interrelated, they have distinct characteristics, so let us look at them:

AspectExposure At DefaultLoss Given Default 
MeaningEAD is an event where a lender expects a total loss from the borrower during default.Loss given default is a certain proportion of the amount to lose when the debt default happens.
PurposeTo calculate the total loss lenders will suffer if all payment fails.It estimates a certain amount that has the possibility of loan repayment failure.
Expressed asDollar or percentageHere, LGD is expressed only in percentage.
ConsequencesIf a borrower defaults, the lender does not account for anything obtained after selling the collateral.Hence, if a lender sells collateral, they must account for the funds obtained.
FormulaEAD = Expected Loss (EL)PD x LGDLGD = 1 - Recovery rate

Frequently Asked Questions (FAQs)

1. Can exposure at default be negative?

In many cases, the EAD value is positive. However, if the value of the collateral is high and exceeds the vast loans, it can give a negative value. In short, if the lender sells the collateral, the yield will be higher than the actual loan amount.

2. Is exposure at default the same as the expected loss?

No, they both have different features. EAD is a value-derived formula to determine the loss of the lender on a given debt failure. In contrast, expected loss estimates the average loss that might be incurred on the entire portfolio. Although they have a calculation involved for loss, they slightly differ.

3. What is exposure at default for derivatives?

EAD for derivatives has two significant components: replacement cost and a potential increase in market value. It helps in determining the overall credit risk in derivative contracts.