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
Hauptverfasser: Baskurt, Burcu, Çelik, Şaban
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.
ISSN:1452-595X
2217-2386
DOI:10.2298/PAN220516019C