Contingent claims analysis as a credit risk metric: Evidence from Turkey
Credit ratings have become open to dispute in recent years regarding their objectivity, timeliness, and the criteria considered in the assignment process, which resulted in an inclination toward other methods to measure credit risk. This study applies contingent claims analysis, a novel risk analysi...
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Veröffentlicht in: | Panoeconomicus 2023, p.19-19 |
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Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Credit ratings have become open to dispute in recent years regarding their
objectivity, timeliness, and the criteria considered in the assignment
process, which resulted in an inclination toward other methods to measure
credit risk. This study applies contingent claims analysis, a novel risk
analysis technique, in Turkey to assess their credit risk appropriately and
investigate the determinants of the sovereign credit risk correctly. While
the technique has been applied in Turkey before, the study contributes to
the results of the preceding literature by applying the technique at a wider
spectrum in terms of regarding the assessed risk indicators, time horizon
considered, diagnosis tests, and sensitivity analyses. Risk indicators are
calculated by applying this method to Turkey between July 2009 and December
2020. Results highlight that the movements in the risk indicators reflect
the market. To ensure robustness, the Spearman rank-order correlations of
the model risk measures with three market indicators are calculated, and
sensitivity analyses are done. The credit default swaps are found to be
correlated with all of the model risk measures, while the distance to
distress is correlated with sovereign bond spreads, affirming model
robustness. Analysis results highlight that among the variables for which
sensitivities are assessed, changes occurring in the volatility of local
currency liabilities heavily impact the risk indicators. Hence, the
contingent claims approach model is robust in considering the correlations
of model risk indicators with actual market data. Therefore, the model can
be used in policymaking for realistic results. |
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ISSN: | 1452-595X 2217-2386 |
DOI: | 10.2298/PAN220516019C |