scholarly journals Impact of the COVID-19 Pandemic on the US Credit Default Swap Market

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-5
Author(s):  
Kirill Romanyuk

The COVID-19 pandemic affected the US economy at different levels. Since credit default swaps can be viewed as a default probability indicator, the article shows the credit default swap market perspective on how the US economy was hit by the pandemic. Forecasting models are built to estimate the predictability of the CDS market sectors during the pandemic, i.e., manufacturing, energy, banks, consumer goods, and services and financial sector excluding banks. Econometric tests are applied to check the uniqueness of credit default swap market sectors after the declaration of the pandemic. The results indicate that the financial sector excluding banks performed uniquely during the pandemic; i.e., the predictability of this sector dropped significantly, and the Chow breakpoint test and Wald coefficient test can identify the shift in the data after declaration of the pandemic.

2019 ◽  
Vol 18 (03) ◽  
pp. 1950021
Author(s):  
Wenting Chen ◽  
Xin-Jiang He ◽  
Xinzi Qiu

In this paper, we consider the valuation of a CDS (credit default swap) contract when the reference asset is assumed to follow a regime-switching model with the volatility allowed to jump among different states. Our motivation originates from empirical evidence demonstrating the existence of regime-switching in real markets. The default probability is analytically derived first, based on which a closed-form formula for the CDS price is obtained so that it can be easily implemented for practical purposes. Finally, numerical experiments are carried out to show quantitatively some properties of the CDS price under the regime-switching model.


2015 ◽  
Vol 02 (04) ◽  
pp. 1550037
Author(s):  
Chih-Wei Lee ◽  
Cheng-Kun Kuo

In this paper, we propose a hybrid method that combines a hazard rate model with the CreditGrades model to value credit default swap (CDS) contracts. The CreditGrades model is considered an industry benchmark for analyzing credit derivatives. The hybrid method makes use of the default probability generated by the CreditGrades model to determine the hazard rate specific to the bond issuing firm. In this way, the hybrid method is empirically shown to produce better CDS forecast.


2009 ◽  
Vol 189 (3) ◽  
pp. 133-140
Author(s):  
Antoine Bouveret

2015 ◽  
Vol 17 (4) ◽  
pp. 71-99 ◽  
Author(s):  
Jenny Castellanos ◽  
Nick Constantinou ◽  
Wing Lon Ng

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