Economic links and credit spreads

Gençay, Ramazan; Signori, Daniele; Xue, Yi; Yu, Xiao; Zhang, Keyi

doi:10.1016/j.jbankfin.2015.02.007

Cited by 37 publications

(14 citation statements)

References 22 publications

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“…This set is also considered by Gençay et al . () in the context of modelling credit spreads of corporates. In the specifics of our model, we consider the average characteristics and financial ratios of Table , calculated on the companies that belong to the first‐order inward neighbourhood of a given company.…”

Section: Network‐based Predictors Of the Credit Risk Of Small And Medmentioning

confidence: 99%

“…To account for this effect, we include in our credit risk model a second set of network variables, which we refer to as firstorder neighbourhood variables. This set is also considered by Gençay et al (2015) in the context of modelling credit spreads of corporates. In the specifics of our model, we consider the average characteristics and financial ratios of Table 3, calculated on the companies that belong to the first-order inward neighbourhood of a given company.…”

Section: Network-based Predictors Of the Credit Risk Of Small And Medmentioning

confidence: 99%

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The Effect of Interfirm Financial Transactions on the Credit Risk of Small and Medium-Sized Enterprises

Vinciotti

Tosetti

Moscone

et al. 2019

Journal of the Royal Statistical Society Series A: Statistics in Society

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Summary Despite the recognized importance of interfirm financial links in determining a company's performance, only a few studies have incorporated proxies for interfirm links in credit risk models, and none of these use real financial transactions. We estimate a credit risk model for small and medium‐sized enterprises, augmented with information on observed interfirm financial transactions. We exploit a novel data set on about 60000 companies based in the UK and their financial transactions over the years 2015 and 2016. We develop several network‐augmented credit risk models and compare their prediction performance with that of a conventional credit risk model that includes only a set of financial ratios. We find that augmenting a default risk model with information on the transaction network makes a significant contribution to increasing the default prediction power of risk models built specifically for small and medium‐sized enterprises. Our results may help bankers and credit scoring agencies to improve the credit scoring of these companies, ultimately reducing their propensity to apply excessive lending restrictions.

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Section: Network‐based Predictors Of the Credit Risk Of Small And Medmentioning

confidence: 99%

Section: Network-based Predictors Of the Credit Risk Of Small And Medmentioning

confidence: 99%

The Effect of Interfirm Financial Transactions on the Credit Risk of Small and Medium-Sized Enterprises

Vinciotti

Tosetti

Moscone

et al. 2019

Journal of the Royal Statistical Society Series A: Statistics in Society

View full text Add to dashboard Cite

show abstract

“…It is important to study the financial connections in the interbank market from the network perspective. The reason for this is that the financial connections can become a channel for propagation and amplification of shocks, which is directly linked to the stability of economic/financial systems [1]. In fact, many empirical studies have shown that interbank lending relationships reflect some typical network structures (e.g., [2][3][4][5][6]), such as random structures, small-world structures, and scale-free structures.…”

Section: Introductionmentioning

confidence: 99%

Network Entropy and Systemic Risk in Dynamic Banking Systems

2017

Complexity

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We investigate network entropy of dynamic banking systems, where interbank networks analyzed include random networks, smallworld networks, and scale-free networks. We find that network entropy is positively correlated with the effect of systemic risk in the three kinds of interbank networks and that network entropy in the small-world network is the largest, followed by those in the random network and the scale-free network.

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“…Real network analysis includes the work of Schweitzer et al (2009), by taking a socioeconomic perspective, they argue a network architecture built upon trade, R&D alliances, ownership or credit-debt relationships can vividly study the strategic behavior of the interacting agents. Gençay et al (2015) use North American supplier-customer network data of public companies to assess counterparty risk and detect counterparty network effects as significant determinants of credit spreads. In the empirical part of Zhu, Pan, Li, Liu & Wang (2016), they test the 'Chinese Twitter' -Xinlang Weibo social network and observe a significant network effect in Chinese social activities.…”

Section: Introductionmentioning

confidence: 99%