Derivation of an indicative rating

Ratings assess the creditworthiness of states, companies, or financial products. They indicate how likely it is that a debtor will reliably meet its obligations.

Ratings are needed to assess the default risk of debtors or financial products. They help investors and lenders make informed decisions about investments and lending. Furthermore, they influence the interest rates and loan conditions for companies and states. Regulators also use ratings to better assess risks in the financial system.

Indicative ratings are non-binding assessments of creditworthiness that have not been officially published or commissioned. They mostly serve as a preliminary assessment of a potential rating. They have no legal or contractual effect, but they provide an initial impression of the expected credit quality. They can later be replaced by an official rating.

The determination can be carried out using a three-stage procedure. In the first stage, a synthetic risk premium for a credit default swap (CDS) is estimated. This is based on an econometric regression model calibrated on a broad sample of companies with actually traded CDS. In the second stage, the synthetic CDS premium obtained is transformed into a risk-neutral one-year probability of default. Methodologically, this follows the hazard-rate approach established in the literature. In the final stage, the one-year probability of default is assigned to a known rating category by comparing it with the idealized standard default rates published by the major rating agencies.

Indicative ratings offer the opportunity to identify the strengths and weaknesses of one's financial position at an early stage. Companies can use them to estimate how investors or rating agencies would assess their creditworthiness. This allows for better preparation for an official rating and, where necessary, targeted improvements to the financial structure. Furthermore, they support the strategic planning of financing or capital market transactions.

The determination of indicative ratings can be challenging if it is based on preliminary or incomplete data. Furthermore, subjective assessments can influence the evaluation.

Methodologically, this can be achieved by utilizing models that have proven reliable in previous research efforts. The use of historical comparative data helps to validate assessments and reduce bias. Furthermore, a multiple assessment using different models should be carried out to compensate for subjective influences. Finally, a documented evaluation of the assumptions and scenarios ensures transparency and traceability.