Literature DB >> 33733095

Spatial Regression Models to Improve P2P Credit Risk Management.

Arianna Agosto1, Paolo Giudici1, Tom Leach1.   

Abstract

Calabrese et al. (2017) have shown how binary spatial regression models can be exploited to measure contagion effects in credit risk arising from bank failures. To illustrate their methodology, the authors have employed the Bank for International Settlements' data on flows between country banking systems. Here we apply a binary spatial regression model to measure contagion effects arising from corporate failures. To derive interconnectedness measures, we use the World Input-Output Trade (WIOT) statistics between economic sectors. Our application is based on a sample of 1,185 Italian companies. We provide evidence of high levels of contagion risk, which increases the individual credit risk of each company.
Copyright © 2019 Agosto, Giudici and Leach.

Entities:  

Keywords:  binary data; contagion; credit risk; spatial autoregressive models; systemic risk

Year:  2019        PMID: 33733095      PMCID: PMC7861317          DOI: 10.3389/frai.2019.00006

Source DB:  PubMed          Journal:  Front Artif Intell        ISSN: 2624-8212


  1 in total

1.  Specification and Estimation of Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances.

Authors:  Harry H Kelejian; Ingmar R Prucha
Journal:  J Econom       Date:  2010-07-01       Impact factor: 2.388

  1 in total
  1 in total

1.  Evaluating borrowers' default risk with a spatial probit model reflecting the distance in their relational network.

Authors:  Jong Wook Lee; So Young Sohn
Journal:  PLoS One       Date:  2021-12-31       Impact factor: 3.240

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.