Assigning Eurozone sovereign credit ratings using CDS spreads

Abstract

Purpose The credit ratings issued by the Big 3 ratings agencies are inaccurate and slow to respond to market changes. This paper aims to develop a rigorous, transparent and robust credit assessment and rating scheme for sovereigns. Design/methodology/approach This paper develops a regression-based model using credit default swap (CDS) data, and data on financial and macroeconomic variables to estimate sovereign CDS spreads. Using these spreads, the default probabilities of sovereigns can be estimated. The new ratings scheme is then used in conjunction with these default probabilities to assign credit ratings to sovereigns. Findings The developed model accurately estimates CDS spreads (based on RMSE values). Credit ratings issued retrospectively using the new scheme reflect reality better. Research limitations/implications This paper reveals that both macroeconomic and financial factors affect both systemic and idiosyncratic risks for sovereigns. Practical implications The developed credit assessment and ratings scheme can be used to evaluate the creditworthiness of sovereigns and subsequently assign robust credit ratings. Social implications The transparency and rigor of the new scheme will result in better and trustworthy indications of a sovereign’s financial health. Investors and monetary authorities can make better informed decisions. The episodes that occurred during the debt crisis could be avoided. Originality/value This paper uses both financial and macroeconomic data to estimate CDS spreads and demonstrates that both financial and macroeconomic factors affect sovereign systemic and idiosyncratic risk. The proposed credit assessment and ratings schemes could supplement or potentially replace the credit ratings issued by the Big 3 ratings agencies.

Systematic and Unsystematic Determinants of Sectoral Risk Default Interconnectedness

“…As an adverse stress scenario, we consider several hypothetical shocks. Following (Ven et al, 2018 ), the harmful situation is based on the tail value of the unconditional probability distribution of a systematic macro component, two hypothetical shocks on the macroeconomic variables are introduced. For real GDP growth, a shock of 7% in each of the four consecutive quarters starting from 2018 Q4 to 2019 Q3 and after one year, the real GDP growth returns to its trend and grows on average by 0.5% the subsequent quarters.…”

Elements of Credit Rating: A Hybrid Review and Future Research Agenda

“…Table 1 represents the cluster classifications of theories and methods used by these 73 researchers to construct credit-rating models. (Mileris & Boguslauskas, 2011) Artificial Intelligence (Dwivedi & Tripathi, 2019), (Shi et al, 2016) (R. H. Abiyev, 2014) (Cao & Xiong, 2014),, (Guo et al, 2012), (Gong, 2017, (Baofeng et al, 2016), , (Estevez & Carballob, 2015), (Al-Najjar & Al-Najjar, 2014), (Guo et al, 2012, (Hongxia et al, 2010), (Shyu, 2008), (Jiang, 2007), (Kao & Wang, 2006), (D. Zhang & Zhang, 2004) Neural Network Random Forest (RF) (Taba et al, 2019) Novel Algorithm Data Mining 5 (Bae & Kim, 2015), (Hristova et al, 2017), (Moradi & rafiei, 2019), (G. Li et al, 2018) Data Mining (Balios et al, 2016), (Fan et al, 2008) Probit Analysis Regression Analysis 23 (Bloechlinger & Leippold, 2018), (Habachi & Benbachir, 2019) Discriminant Analysis (Polito & Wickens, 2014) Default probabilities (Doumpos et al, 2015) (Mileris & Boguslauskas, 2011), (Guotai & Zhipeng, 2017, (FourieI et al, 2016), (Evgenidis et al, 2016), (FourieI et al, 2016, (Ven et al, 2018), (Di...…”