Bank lending and systemic risk: A financial-real sector network approach with feedback

Silva, Thiago Christiano; Alexandre, Michel; Tabak, Benjamin Miranda

doi:10.1016/j.jfs.2017.08.006

Cited by 73 publications

(32 citation statements)

References 32 publications

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“…The result is a customer experience that encourages better engagement 2 Complexity and reduces churn. Bank lending and systemic risk [28,29] is another issue in economics sector that attracted a lot of attention. This model will find empirical evidence against diversification as a mean to reduce systemic risk.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Approach Using Oversampling Technique and Cost‐Sensitive Learning for Bankruptcy Prediction

et al. 2019

Complexity

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The diagnosis of bankruptcy companies becomes extremely important for business owners, banks, governments, securities investors, and economic stakeholders to optimize the profitability as well as to minimize risks of investments. Many studies have been developed for bankruptcy prediction utilizing different machine learning approaches on various datasets around the world. Due to the class imbalance problem occurring in the bankruptcy datasets, several special techniques would be used to improve the prediction performance. Oversampling technique and cost-sensitive learning framework are two common methods for dealing with class imbalance problem. Using oversampling techniques and cost-sensitive learning framework independently also improves predictability. However, for datasets with very small balancing ratios, combining two above techniques will produce the better results. Therefore, this study develops a hybrid approach using oversampling technique and cost-sensitive learning, namely, HAOC for bankruptcy prediction on the Korean Bankruptcy dataset. The first module of HAOC is oversampling module with an optimal balancing ratio found in the first experiment that will give the best overall performance for the validation set. Then, the second module uses the cost-sensitive learning model, namely, CBoost algorithm to bankruptcy prediction. The experimental results show that HAOC will give the best performance value for bankruptcy prediction compared with the existing approaches.

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Section: Introductionmentioning

confidence: 99%

A Hybrid Approach Using Oversampling Technique and Cost‐Sensitive Learning for Bankruptcy Prediction

et al. 2019

Complexity

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“…Financial systems, being complex networks in their nature, evolve continuously and their features undergo a neverending process of substantial changes (see, e.g., Tabak et al [1]). There is a vast body of recent literatures on systemic financial risk arising from interconnectedness of institutions, targeting financial systems' stability (see, e.g., Aymann et al [2]; Souza et al [3]; Silva et al [4]). One of the important domains analyzed in literature is the case of financial networks primarily linked through different types of credit contracts, portfolio contagion, or credit guarantees, (see, e.g., Li and Wen [5], Anagnostou et al [6], and Jiang and Fan [7]).…”

Section: Introductionmentioning

confidence: 99%

Weight of the Default Component of CDS Spreads: Avoiding Procyclicality in Credit Loss Provisioning Framework

Gubareva

2019

Complexity

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The current expected loss calculations have recently attracted considerable attention in the research on credit risk modeling, impairment provisioning, and financial networks’ stability. A new CDS-based approach to estimate current expected credit loss is proposed for low default portfolios, containing credit exposures to corporate issuers covered by publicly traded CDS contracts. First, a fraction of CDS spread related to a pure default compensation for different CDS maturities is assessed. Our results contrast with previous research. Second, based on the obtained historical weights of the default risk premium, a forward-looking term structure of the probabilities of default implied by the current CDS quotes is derived. The proposed approach covers both investment and noninvestment grade debt. The resulting framework is applied to a sample of corporate bonds. The developed methodology provides a useful tool, on one hand, for credit risk managers and balance-sheet preparers and, on the other hand, for regulators of financial markets as it sheds light on how procyclicality could be avoided in provisions.

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“…Few papers have used financial institution level, network-based proxies for systemic risk (Thurner and Poledna (2013)), because it is very hard to acquire this granular level of data. The benefit of doing so, which at the same time is limitation of other non-network-based proxies (Blanco et al, 2018;Silva et al, 2018;Kanno, 2016), is that the DebtRank account for the contagion between financial institutions. 12…”

Section: Effects On Banks and Systemic Riskmentioning

confidence: 99%

Reciprocal Lending Relationships Between Financial Conglomerates: Evidence from the Mexican Repo Market

2019

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This paper examines the reciprocal lending between Financial Conglomerates in the repo market to better understand both what motivates powerful firms to engage in this type of contemporaneous crossfunding relationships, and, on the other hand, some of the implications that such reciprocal transactions may entail for the agents involved and for the market as a whole. In particular, in terms of the implications we focus on two dimensions: first, the potential effects that reciprocal lending has on the market power of FCs and the competitiveness of the repo market for mutual funds and second, the potential implications that frequent and stable reciprocal lending can have in terms of the industry's systemic risk. Using transaction-level data from the Mexican repo market, we show that reciprocal lending between financial conglomerates is mutually beneficial as it reduces search costs for borrowers and mitigates credit risk concerns for lenders. Further, we find that reciprocal lending favors market concentration of the repo lending in a few powerful funds and increases fund market power. Finally, we find that reciprocal lending also leads to centrality within the financial network and increases the dependence between the parties involved. Interestingly, a higher intensity of reciprocal lending can harmful, but this does not necessarily deteriorates financial stability.

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Bank lending and systemic risk: A financial-real sector network approach with feedback

Cited by 73 publications

References 32 publications

A Hybrid Approach Using Oversampling Technique and Cost‐Sensitive Learning for Bankruptcy Prediction

A Hybrid Approach Using Oversampling Technique and Cost‐Sensitive Learning for Bankruptcy Prediction

Weight of the Default Component of CDS Spreads: Avoiding Procyclicality in Credit Loss Provisioning Framework

Reciprocal Lending Relationships Between Financial Conglomerates: Evidence from the Mexican Repo Market

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