scholarly journals Sovereign Credit Risk Assessment with Multiple Criteria Using an Outranking Method

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Diogo F. de Lima Silva ◽  
Julio Cezar Soares Silva ◽  
Lucimário G. O. Silva ◽  
Luciano Ferreira ◽  
Adiel T. de Almeida-Filho
Keyword(s):  
Risk Assessment ◽  
Default Risk ◽  
Numerical Application ◽  
Rating Agencies ◽  
Sovereign Credit ◽  
Outranking Method ◽  
Proposed Model ◽  
Sovereign Credit Risk ◽  
Multicriteria Sorting

In view of the records of failures in rating agencies’ assessments for sorting countries’ quality of credit in degrees of default risk, this paper proposes a multicriteria sorting model using reference alternatives so as to allocate sovereign credit securities into three categories of risk. From a numerical application, what was observed from the results was a strong adherence of the model in relation to those of the agencies: Standard & Poor's and Moody's. Since the procedure used by the agencies is extremely subjective and often questioned, the contribution of this paper is to put forward the use of an objective and transparent methodology to sort these securities. Given the agencies’ conditions for undertaking the assessment, a complete similarity between the results obtained and the assignments of the agencies was not expected. Therefore, this difference arises from subjective factors that the agencies consider but the proposed model does not. Such subjective and questionable aspects have been partly responsible for the credibility of these credit agencies being diminished, especially after the 2007-2008 crisis.

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 How Sovereign Is Sovereign Credit Risk?

2011 ◽  
Vol 3 (2) ◽  
pp. 75-103 ◽  
Author(s):  
Francis A Longstaff ◽  
Jun Pan ◽  
Lasse H Pedersen ◽  
Kenneth J Singleton
Keyword(s):  
Credit Risk ◽  
Risk Premium ◽  
Default Risk ◽  
Principal Component ◽  
Credit Spread ◽  
Credit Spreads ◽  
High Yield ◽  
Sovereign Credit ◽  
The Us ◽  
Sovereign Credit Risk

We study the nature of sovereign credit risk using an extensive set of sovereign CDS data. We find that the majority of sovereign credit risk can be linked to global factors. A single principal component accounts for 64 percent of the variation in sovereign credit spreads. Furthermore, sovereign credit spreads are more related to the US stock and high-yield markets than they are to local economic measures. We decompose credit spreads into their risk premium and default risk components. On average, the risk premium represents about a third of the credit spread. (JEL F34, G15, O16, O19, P34)

Model and Method for Contributor’s Quality Assessment in Community Image Tagging Systems

2018 ◽  
pp. 45-51
Author(s):  
A. V. Ponomarev
Keyword(s):  
Large Scale ◽  
Wide Spectrum ◽  
Preference Relation ◽  
Pairwise Comparison ◽  
Ground Truth ◽  
Comparison Method ◽  
Characteristic Matrix ◽  
Image Tagging ◽  
Proposed Model

Introduction: Large-scale human-computer systems involving people of various skills and motivation into the information processing process are currently used in a wide spectrum of applications. An acute problem in such systems is assessing the expected quality of each contributor; for example, in order to penalize incompetent or inaccurate ones and to promote diligent ones.Purpose: To develop a method of assessing the expected contributor’s quality in community tagging systems. This method should only use generally unreliable and incomplete information provided by contributors (with ground truth tags unknown).Results:A mathematical model is proposed for community image tagging (including the model of a contributor), along with a method of assessing the expected contributor’s quality. The method is based on comparing tag sets provided by different contributors for the same images, being a modification of pairwise comparison method with preference relation replaced by a special domination characteristic. Expected contributors’ quality is evaluated as a positive eigenvector of a pairwise domination characteristic matrix. Community tagging simulation has confirmed that the proposed method allows you to adequately estimate the expected quality of community tagging system contributors (provided that the contributors' behavior fits the proposed model).Practical relevance: The obtained results can be used in the development of systems based on coordinated efforts of community (primarily, community tagging systems). 

In Sickness and in Debt: The COVID-19 Impact on Sovereign Credit Risk

2020 ◽  
Author(s):  
Patrick Augustin ◽  
Valeri Sokolovski ◽  
Marti G. Subrahmanyam ◽  
Davide Tomio
Keyword(s):  
Credit Risk ◽  
Sovereign Credit ◽  
Sovereign Credit Risk

scholarly journals Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks

2021 ◽  
Vol 11 (6) ◽  
pp. 2838
Author(s):  
Nikitha Johnsirani Venkatesan ◽  
Dong Ryeol Shin ◽  
Choon Sung Nam
Keyword(s):  
Neural Network ◽  
Radiation Dose ◽  
Convolutional Neural Network ◽  
Model Performance ◽  
Performance Comparison ◽  
Apache Spark ◽  
Training Time ◽  
Learning Framework ◽  
Proposed Model

In the pharmaceutical field, early detection of lung nodules is indispensable for increasing patient survival. We can enhance the quality of the medical images by intensifying the radiation dose. High radiation dose provokes cancer, which forces experts to use limited radiation. Using abrupt radiation generates noise in CT scans. We propose an optimal Convolutional Neural Network model in which Gaussian noise is removed for better classification and increased training accuracy. Experimental demonstration on the LUNA16 dataset of size 160 GB shows that our proposed method exhibit superior results. Classification accuracy, specificity, sensitivity, Precision, Recall, F1 measurement, and area under the ROC curve (AUC) of the model performance are taken as evaluation metrics. We conducted a performance comparison of our proposed model on numerous platforms, like Apache Spark, GPU, and CPU, to depreciate the training time without compromising the accuracy percentage. Our results show that Apache Spark, integrated with a deep learning framework, is suitable for parallel training computation with high accuracy.

scholarly journals Central bank gold reserves and sovereign credit risk

2021 ◽  
pp. 102127
Author(s):  
Sawan Rathi ◽  
Sanket Mohapatra ◽  
Arvind Sahay
Keyword(s):  
Central Bank ◽  
Credit Risk ◽  
Sovereign Credit ◽  
Sovereign Credit Risk
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