Redesigning Credit Derivatives to Better Cover Sovereign Default Risk

2012 ◽  
Author(s):  
Darrell Duffie ◽  
Mohit Thukral
Keyword(s):  
Default Risk ◽  
Credit Derivatives ◽  
Sovereign Default ◽  
Sovereign Default Risk

Ricardian Equivalence and Sovereign Default Risk

2017 ◽  
Author(s):  
Stefan Eichler ◽  
Ju Hyun Pyun
Keyword(s):  
Default Risk ◽  
Sovereign Default ◽  
Ricardian Equivalence ◽  
Sovereign Default Risk

Fiscal Policy and Interest Rates: The Role of Sovereign Default Risk

2010 ◽  
Vol 7 (1) ◽  
pp. 7-30 ◽  
Author(s):  
Thomas Laubach
Keyword(s):  
Fiscal Policy ◽  
Interest Rates ◽  
Default Risk ◽  
Sovereign Default ◽  
Sovereign Default Risk

scholarly journals Fiscal commitment and sovereign default risk

2021 ◽  
Author(s):  
Siming Liu ◽  
Hewei Shen
Keyword(s):  
Default Risk ◽  
Sovereign Default ◽  
Sovereign Default Risk

Banks’ international assets and sovereign default risk

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Filippo Gori
Keyword(s):  
Credit Risk ◽  
Default Risk ◽  
Sovereign Default ◽  
Content Type ◽  
Sovereign Credit ◽  
Developed Economies ◽  
Sovereign Default Risk ◽  
Sovereign Credit Risk ◽  
Using Data ◽  
International Assets

Purpose This paper aims to investigate the nexus between banks’ foreign assets and sovereign default risk in a panel of 15 developed economies. The empirical evidence suggests that banks’ foreign exposure is an important determinant of sovereign default probability. Design/methodology/approach Using data from the consolidated banking statistics (total foreign claims on ultimate risk basis) by the Bank of International Settlements, the author constructs a measure of bank international exposure to peer countries. This measure is then used as the target variable in a panel regression for sovereign credit default swaps. The model includes 15 European and non-European developed economies. Identification is discussed extensively in the paper. Findings Quantitatively, a 1% increase in banks’ cross-border claims increases sovereign default risk by about 0.19%. The relationship is weaker when banks are more capitalised. On the other hand, governments are more vulnerable to credit risk spillovers from banks’ international portfolios when having higher debt to GDP ratios. Originality/value To the best of the author’s knowledge, this is the first paper that attempts explicitly to establish an empirical connection between banks’ international assets and sovereign default risk. To the author’s opinion, this paper represents a contribution to our understanding of how sovereign credit risk spills over across countries. It also extends significantly the existing literature on the determinants of sovereign risk (that primarily focused on fundamentals, market characteristics – such as liquidity – and global factors). This paper ultimately sheds some new light on the role of intermediaries in the international transmission of credit risk, also adding to today’s discussion about the linkages between banks and sovereigns.

scholarly journals Escape Routes from Sovereign Default Risk in the Euro Area

2015 ◽  
Author(s):  
Willi Semmler ◽  
Christian R. Proaao
Keyword(s):  
Euro Area ◽  
Default Risk ◽  
Sovereign Default ◽  
Sovereign Default Risk

scholarly journals Uncertainty Premia, Sovereign Default Risk, and State-Contingent Debt

2021 ◽  
Vol 2021 (076) ◽  
pp. 1
Author(s):  
Francisco Roch ◽  
Francisco Roldán
Keyword(s):  
Default Risk ◽  
Sovereign Default ◽  
Sovereign Default Risk

scholarly journals Mexican Stock Exchange Performance after the Crisis of 2008: Application of Data Mining

2020 ◽  
Vol 18 ((1)) ◽  
Author(s):  
Eliseo Ramírez Reyes ◽  
Arturo Morales Castro ◽  
Néstor Juan Sanabria Landazábal
Keyword(s):  
Neural Networks ◽  
Default Risk ◽  
Regression Models ◽  
Prediction Models ◽  
Stock Exchange ◽  
Sovereign Default ◽  
Linear Regression Models ◽  
Exchange Performance ◽  
Sovereign Default Risk ◽  
Dow Jones Industrial Average

Different prediction models are explored to analyze the performance of the Mexican Stock Exchange (PQI) after the 2008 crisis. These models have demonstrated a good prognostic capacity for both multivariable and univariable approaches given their non-parametric characteristics. The selected variables were: Dow Jones Industrial Average Index (DJIA), CPI, International Reserves (IR), CETES28, USDMX exchange rate, (M1) and the sovereign default risk of Mexico (MRDS). The models were evaluated with MAPE and compared with linear regression models (LR) and neural networks (NN). The results show that the models have a similar performance according to the percentages of error they presented.

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