Structural credit risk models and the determinants of credit default swap spreads

• Autor(a) principal: Lourenço, Rodrigo Sant'Ana
• Data de Publicação: 2021
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10071/24068
• topic: Risco de crédito -- Credit risk; Structural models; Credit default swap; Credit spreads; Panel data models; Modelos estruturais; Spread de crédito; Modelos de dados em painel

Following the financial innovation and the consequences of the recent 2008-2009 global financial crisis, the interest and resources allocated into measuring and modelling credit risk has seen a major increase by researchers and practitioners over the last decades. The main objective of this thesis is to explore the determinants of credit spreads, analysing first the performance of theoretical variables of default risk in explaining credit default swap (CDS) spreads, while introducing other firm-specific, macroeconomic, liquidity and credit rating factors. The dataset used is composed of non-financial European companies, for the period of 2010 to 2018 and we use panel data models to perform the econometric analysis. In addition, this study also analyses structural credit risk models, namely the Merton (1974) model and some of its limitations and extensions. Our empirical results show that theoretical determinants are statistically and economically significant and are able to explain 27% of the observed CDS spreads levels. After controlling for market liquidity, credit rating, firm and market-specific factors, we are capable of explaining 57% of the total CDS spreads levels and 21% of the spreads changes. Moreover, by performing a robustness analysis, we conclude that the investigated determinants perform better in explaining credit spreads, when the overall level of credit risk in the market is higher, which is consistent with previous evidence. Lastly, our results suggest that structural credit risk models would benefit if they were further developed to account for both theoretical and non-theoretical factors, such as macro-financial variables.