The approaches presented are primarily tools to assess entities’ ability to service debt. Sovereign risk managers need to complement this analysis with a view on the drivers of willingness to repay which may be dependent on the institutional relationship with various parts of the government. Also, the willingness to repay may be driven by the respective arrangement. For example, willingness to repay may differ between credit guarantees and on-lending, as in some instances a default on payments to the government in the case of on-lending may be associated with lower default costs as a default on payments to commercial creditors. Willingness to repay may be estimated based on the historic relationship between the government and the beneficiary.

Irrespective of the credit risk analysis approach used, risk managers should critically assess the results obtained, reflect on the assumptions made and modeling techniques employed. The results obtained from credit risk analysis should not be blindly taken at face value but risk managers should be aware of the limitations of the model used and should complement the results from a risk analysis model with common sense, experience of risk managers, and other information available.

# **2.3 Credit risk quantification**

The analysis of credit risk forms the basis for quantification. Credit risk analysis may already result in some quantified measures (e.g. default probabilities (PDs) in statistical models, distance to default in structural models that can be translated into PDs, losses attached to certain probabilities in scenario analysis) or ordinal measures (e.g. rating in credit scoring).

Policy makers, however, likely have an interest in understanding the cost of guarantees and on-lending in nominal amounts and to estimate the potential impact of guarantees and on-lending on the government’s budget and balance sheet, including the sustainability of government finances. Results from credit risk analysis can be converted into nominal amounts or prices using two primary approaches. The first uses differentials in market prices. The value of a guarantee is seen as the difference between the price of a risky debt instrument (non-guaranteed) and the price of a risk-free debt instrument (guaranteed by the government) with the same characteristics. The prices of the respective debt instruments can be directly observed if the beneficiary entity has guaranteed and non-guaranteed traded debt outstanding. If this is not the case, market prices for proxies with similar risk characteristics (e.g. rating) may be used. Market information aggregated from rating agencies may be useful to compare average bond spreads for the beneficiary with those of the guarantor, as shown in Figure 2.4.<sup>25</sup> Using price differentials has the advantage of basing risk estimates on prices actually transacted in the market. If prices are available, the quantification of risk is fairly straightforward. On the other hand, market prices may not reflect the government’s risk exposure if they reflect implicit government support.

The second approach prices risk bottom-up through the use of default probabilities. Expected losses are estimated by multiplying PDs with the respective exposure at default (EAD) and loss given default (LGD). Loss given default is an estimate of the share of the guaranteed/on-lent amount the guarantor/onlender has to undertake in the event of default that cannot be recovered later (e.g. through the collection of receivables). This bottom-up approach requires an estimation of all three components described above.

> 25 Alternatively, CDS spreads may be used. Both bond spreads and CDS spreads may be an imperfect proxy for loan spreads. Also, spreads fluctuate over time, hence a longer term average may be useful for the context of sovereign guarantees/on-lending.

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