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An R-Based Landscape Validation of a Competing Risk Model
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Spatial Regression Models to Improve P2P Credit Risk Management.

Arianna Agosto1, Paolo Giudici1, Tom Leach1

  • 1Department of Economics and Management, University of Pavia, Pavia, Italy.

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|March 18, 2021
PubMed
Summary

This study measures corporate contagion effects using spatial regression models and World Input-Output Trade data. Findings reveal significant contagion risk, increasing individual company credit risk.

Keywords:
binary datacontagioncredit riskspatial autoregressive modelssystemic risk

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Area of Science:

  • Economics
  • Financial Risk Management
  • Econometrics

Background:

  • Binary spatial regression models effectively measure contagion effects in credit risk, as demonstrated by Calabrese et al. (2017) using banking data.
  • Understanding contagion effects is crucial for assessing systemic risk in financial markets.

Purpose of the Study:

  • To apply a binary spatial regression model to quantify contagion effects stemming from corporate failures.
  • To utilize World Input-Output Trade (WIOT) statistics for deriving interconnectedness measures between economic sectors.

Main Methods:

  • Application of a binary spatial regression model.
  • Utilizing World Input-Output Trade (WIOT) data for 1,185 Italian companies to measure inter-sectoral economic linkages.
  • Analysis of contagion risk in corporate credit risk.

Main Results:

  • Evidence of high levels of contagion risk among Italian companies.
  • Demonstration that contagion effects significantly increase individual company credit risk.
  • Quantification of interconnectedness through WIOT data.

Conclusions:

  • Corporate failures can trigger significant contagion effects within the economy.
  • The applied methodology provides a robust framework for assessing contagion risk in corporate networks.
  • Findings underscore the importance of considering inter-company dependencies in credit risk assessment.