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.
Highlights
In recent years, the emergence of financial technologies is redefining the roles of financial intermediaries and introducing many opportunities for consumers and investors
In a P2P lending platform, credit risk is determined by the platform but the risk is fully borne by the lender
We focus on the Generalized Method of Moments (GMM) proposed by Pinkse and Slade (1998)
Summary
The emergence of financial technologies (fintechs) is redefining the roles of financial intermediaries and introducing many opportunities for consumers and investors. When financial networks are backed by statistical models, inferential statements can be obtained Important contributions in this framework are Billio et al (2012); Diebold and Yilmaz (2014); Hautsch et al (2015); Ahelegbey et al (2016); Giudici and Spelta (2016), and Giudici and Parisi (2018), who propose measures of connectedness based on similarities, Grangercausality tests, variance decompositions and partial correlations between market price variables. We improve these contributions, extending them to the P2P context and linking network models, that are often merely descriptive, with econometric models, providing a predictive framework.
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