scholarly journals THE BANKRUPTCY RISK IN INFRASTRUCTURE SECTORS: AN ANALYSIS FROM 2006 TO 2018

2021 ◽  
Vol 22 (4) ◽  
Author(s):  
VANDERSON A. DELAPEDRA-SILVA

ABSTRACT Purpose: This research aims to identify the probability of default of infrastructure companies considering the sector specificities of their activities. In addition, the work seeks to identify the application of structural variables of probability of default in a model in a reduced way in order to identify the significance of its use. For this purpose, we investigated 1,520 North American companies from six different sectors linked to infrastructure. Originality/value: The analyzes carried out to identify the probability of a company going bankrupt hardly consider its sectorial particularity. Although most models bring important inputs for risk assessment, most of them do not consider this sectoral view. Then, this work has as value and originality the contribution to fill this gap and identify the existence of sectorial differences in the analysis of default risk in infrastructure companies in the North American market in the period between 2006 and 2018. Design/methodology/approach: The study performed a logistic regression (logit model) using 11 model variables established in calculating the probability of default. It also used the variable distance to default as an explanatory variable in order to identify its ability to explain the researched phenomenon. Findings: The study identified that, in addition to the size of the companies, the distance to default variable is the only variable that can be applied with significance in all the analyzed sectors. In addition, it was identified that companies in the oil and gas sector have less sensitivity to this variable than companies in the other sectors.

2009 ◽  
pp. 74-85 ◽  
Author(s):  
A. R. Miller ◽  
W. F. Grazer

2000 ◽  
Vol 11 (4) ◽  
pp. 207-215 ◽  
Author(s):  
Chantal Bergeron ◽  
John F. Livesey ◽  
Dennis V.C. Awang ◽  
John T. Arnason ◽  
Jatinder Rana ◽  
...  

2012 ◽  
Vol 45 (2) ◽  
pp. 1166-1174 ◽  
Author(s):  
Elisabetta Lambertini ◽  
Michelle D. Danyluk ◽  
Donald W. Schaffner ◽  
Carl K. Winter ◽  
Linda J. Harris

2005 ◽  
Vol 3 (1) ◽  
pp. 123
Author(s):  
Alba Regina Moretti ◽  
Beatriz Vaz de Melo Mendes

The modeling of the extremal dependence structure can be made through parametric models classified in two families: Logistic and Mixed, which contain the symmetric and asymmetric models. The bivariate models are very useful in practical applications on the extreme value theory, in particular in a financial area. Considering the strong influence of the North American market on other financial markets, we investigate how does the dependence structure among the Latin American markets change after filtering the influence of the North American market. To remove that influence, we carry on a polynomial regression with GARCH (1,1) errors, and fit the bivariate extreme value models to the pairs of monthly maxima and minima of the standardized regression residuals.


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