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Credit Analysis
Project Finance How To Weigh High Yield Against Expected Loses

Sep. 02 2018 — In what seems like a perennial state of low interest rates, there appears to be no end in sight to market participants’ search for income. Decreasing bond yields – some markets even flirting with negative yields – has meant the search continues to expand away from the traditional bond markets and towards alternatives, which may offer steady income.

An area where we are seeing increasing interest from market players is the higher yielding project finance industry with its steady projected cash flows, which offers some means to achieving returns not available elsewhere in the current yield-tight environment.

There is, however, the perception that higher yield projects will involve the risk of higher losses, and as with most investments, higher returns can go hand-in-hand with additional risks. Although past performance does not guarantee future results, returns may be influenced by a number of factors, including the asset class itself.

In the case of project finance, market players should understand the importance of reliable risk measurement calculations.

Computing Expected Losses (EL): Understanding the several components

From conversations with clients and other market participants, we understand worries over high expected losses may be a deterrent due to a cautious assessment being performed to determine the risks involved.

A better understanding involves looking at the fundamentals behind the credit analysis.

Starting with the basics…We know that:

Expected Loss (EL) = Probability of Default (PD) x Loss Given Default (LGD) x Exposure at Default (EAD)


  • Probability of default (PD): Probability that an investment will default on an agreement (i.e. not pay when due);
  • Loss Given Default (LGD): Proportion of investment lost due to default;
  • Exposure at Default (EAD): Exposure “at risk” of loss at date of calculation.

There is a great focus on the PD associated with any given investment, which in turn depends on the entity’s credit quality. Generally, the better the credit quality - the lower the expected PD.

However, as we can see from the formula above, to fully understand the EL it is necessary to consider the recovery prospects (i.e., the LGD).

An informed decision regarding EL should encompass both the PD and LGD assessment. The interaction between the two components for the purpose of computing the EL is illustrated in the figure below:

Computing Expected Losses (EL): Relationship between Probability of Default (PD) and Loss Given Default (LGD)

Source: S&P Global Market Intelligence. For Illustrative purposes only.

In an ideal scenario where both PD and LGD are low, the probability of the investment defaulting is very low. If it ends up defaulting, the recoveries are close to 100%. Moreover, it is easy to understand that the EL is very low, reflecting the low risk of these investments. Inversely, if both PD and LGD are high, the EL will reflect the higher risk associated with these investments.

The assessment of EL is harder to perform when one of these components displays a high risk reading and the other reads low risk. In these cases, it is very important to fully understand both risks in order to make an informed decision.

When an investor decides to move into the high yield space (e.g., investments that have lower credit quality and hence higher PDs), the key determinant for computing EL is no longer the credit quality assessment, but rather the collateral value and security enforceability. In these cases, given that the investment has a higher likelihood of defaulting, the EL is largely determined by the LGD assessment (how much can you recover).

In certain cases, a high PD might not necessarily mean that the EL is high. If the LGD is low (your recoveries are high), then the EL can actually be low as well.

If the investments are in project finance, the LGD associated will typically tend to be low , leading to low EL even within the high yield space. This may explain why we have noticed an increase in new market participants (for example, insurance companies) in this asset class over the last few years.

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