Does the utility industry have the financial strength sufficient to meet the combined challenges of: (1) sharply increasing and highly volatile fuel and purchased-power costs; (2) significant...
Measuring and Managing Utility Credit Risk: Taking a Page from Wall Street
this is an expectation, it is not risk, and should be built into the cost of a transaction. The second metric gets to the heart of credit risk and is referred to as economic capital (EC). Where EL measures the anticipated average loss from a portfolio over the relevant time horizon, EC captures the variance or the uncertainty of the losses around the average. With its focus on uncertainty, EC quantifies the portfolio credit risk. These concepts are depicted in the loss distribution in Figure 3.
Expected Loss. EL is measured by multiplying together three factors, probability of default (PD), expected exposure (EE), and loss given default (LGD). The logic behind multiplying these factors together is straightforward, as depicted in Figure 4.
The probability of default is determined by a counterparty's or customer's credit quality. In the case of large, long-term deals with energy marketers, this can be based upon credit quality measures such as agency debt ratings. The way to measure expected exposure depends upon the nature of the exposure. For retail customers, this could be measured by current accounts receivable. In contrast, for merchant energy counterparties, the appropriate measure is not only accounts receivables, but also current mark-to-market exposure of contracts, plus the expected potential future exposure of contracts. Loss given default is determined by what remedies may be needed to mitigate credit losses (e.g., letter of credit, parent guarantee). The resulting EL calculated for each individual counterparty in a portfolio is additive across all counterparties to identify the portfolio level EL.
Economic Capital. Economic capital is a bit trickier. To be specific, EC is a measure of the amount of resources a firm must maintain to cover a "worst case" credit loss, and still remain solvent. It should be clear that the amount of EC is driven by how an organization defines a "worst case" loss. The drivers of EC for a "worst case" loss are the same three drivers of EL, plus two more:
- Counterparty/customer credit quality-The more likely a customer or counterparty is to default, the higher the "worst case" loss is.
- Expected exposure-The more exposure to credit losses obviously leads to higher amounts of "worst case" loss.
- Loss given default-The less that can be recovered from defaulted exposures, the higher the loss given default and "worst case" loss will be.
- Portfolio concentration and correlation-Having exposures to limited numbers of counterparties or concentrations in certain types of counterparties is like having all your eggs in one basket. This results in potentially large amounts of losses when things start going bad, resulting in larger "worst case" losses.
- Target debt rating-The more creditworthy an institution wants to be, the more willing it needs to be to cover an increasingly worsening "worst case" scenario. In other words, an "A" rated institution has to be able to weather a worse "worst case" loss than does a "B" rated institution.
A properly developed EC framework incorporates these drivers in such a way to quantify the credit risk of an entire portfolio and (more usefully) attribute the credit risk back to the individual