CDS-based implied probability of default estimation. Issue 4 (21st July 2020)

• Record Type: Journal Article
• Authors: Abid, Amira; Abid, Fathi; Kaffel, Bilel
• Is Part Of: Journal of risk finance. Volume 21:Issue 4(2020)
• Page Start: 399
• Page End: 422
• Publication Date: 2020-07-21
• Subjects: CDS -- Market-implied default probability -- Term structure -- Implied rating -- Transition matrix; G24 -- G32; Risk management -- Periodicals; Risk (Insurance) -- Periodicals; Risk assessment -- Periodicals; 658.155
• DOI: 10.1108/JRF-05-2019-0079
• Languages: English
• ISSNs: 1526-5943

Abstract

Abstract : Purpose: This study aims to shed more light on the relationship between probability of default, investment horizons and rating classes to make decision-making processes more efficient. Design/methodology/approach: Based on credit default swaps (CDS) spreads, a methodology is implemented to determine the implied default probability and the implied rating, and then to estimate the term structure of the market-implied default probability and the transition matrix of implied rating. The term structure estimation in discrete time is conducted with the Nelson and Siegel model and in continuous time with the Vasicek model. The assessment of the transition matrix is performed using the homogeneous Markov model. Findings: The results show that the CDS-based implied ratings are lower than those based on Thomson Reuters approach, which can partially be explained by the fact that the real-world probabilities are smaller than those founded on a risk-neutral framework. Moreover, investment and sub-investment grade companies exhibit different risk profiles with respect of the investment horizons. Originality/value: The originality of this study consists in determining the implied rating based on CDS spreads and to detect the difference between implied market rating and the Thomson Reuters StarMine rating. The results can be used to analyze credit risk assessments and examine issues related to the Thomson Reuters StarMine credit risk model.