Diversifying away risks through derivatives: an analysis of the Italian banking system

Infante, Luigi; Piermattei, Stefano; Santioni, Raffaele; Sorvillo, Bianca

doi:10.1007/s40888-020-00180-x

Cited by 9 publications

(4 citation statements)

References 41 publications

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“…the amount of interbank and retail deposits. Retail deposits are generally considered as an indicator of the type of specialization of the banks: the more a bank relies on retail funding, the more it tends to be specialized in traditional credit activities to the private sector rather than other forms of financial investments (Infante et al, 2020;Farné and Vouldis, 2017;Altunbas et al, 2011). Moreover, a business model where funding is mostly based on retail deposits instead of the recourse to the wholesale interbank market is most of the time associated with a more stable pattern of loans on the asset side due to the higher stability of the former with respect to the latter, especially during periods of crisis.…”

Section: The Qrrf Methodology and The Specification Of The Modelmentioning

confidence: 99%

Learning from Revisions: A Tool for Detecting Potential Errors in Banks' Balance Sheet Statistical Reporting

2021

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Ensuring and disseminating high-quality data is crucial for central banks to adequately support monetary analysis and the related decision-making process. In this paper we develop a machine learning process for identifying errors in banks' supervisory reports on loans to the private sector employed in the Bank of Italy's statistical production of Monetary and Financial Institutions' (MFI) Balance Sheet Items (BSI). In particular, we model a "Revisions Adjusted -Quantile Regression Random Forest" (RA-QRRF) algorithm in which the predicted acceptance regions of the reported values are calibrated through an individual "imprecision rate" derived from the entire history of each bank's reporting errors and revisions collected by the Bank of Italy. The analysis shows that our RA-QRRF approach returns very satisfying results in terms of error detection, especially for the loans to the households sector, and outperforms wellestablished alternative outlier detection procedures based on probit and logit models.

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Section: The Qrrf Methodology and The Specification Of The Modelmentioning

confidence: 99%

Learning from Revisions: A Tool for Detecting Potential Errors in Banks' Balance Sheet Statistical Reporting

2021

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“…Moreover, Shim and Zhu (2014) provide evidence to the hypothesis that CDS trading helps develop new hedging possibilities in Asian nations and increases information transparency for investors by using data samples from five Asian economies: Hong Kong SAR, Japan, Korea, Malaysia, and Singapore. In a similar spirit, Cyree et al (2012) and Infante et al (2020) document that banks primarily use derivatives to hedge credit risk, and their use causes banks' performance to be countercyclical owing to the risk-sharing component.…”

Section: Opportunity To Hedge Credit Risk With Cdsmentioning

confidence: 98%

“…(2012) and Infante et al. (2020) document that banks primarily use derivatives to hedge credit risk, and their use causes banks’ performance to be countercyclical owing to the risk‐sharing component.…”

Section: Opportunity To Hedge Credit Risk With Cdsmentioning

confidence: 99%

Why do banks use credit default swaps (CDS)? A systematic review

Tabassum

Yameen

2023

Journal of Economic Surveys

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Credit default swaps (CDS)—the fiercely discussed derivatives instrument since the explosion of the recent global credit crunch—are still subject to considerable theoretical and practical debate. More than a decade after significant developments in CDS literature, we currently lack a holistic understanding of the disparate strands of evidence, synthesizing analytical advancements in the field. We, therefore, conducted a systematic review of 143 publications (peer‐reviewed and first‐tier grey literature), focusing on three key areas: CDS characteristics and development, credit risk hedging, and determinants of CDS use, and performed the first bibliometric mapping for CDS. Our review makes the following contributions. First, measuring CDS characteristics and development gives a snapshot of how CDS remained a significant indicator for hedging corporate lending risk. Second, by mapping the research landscape, we provide insights into banks’ use of CDS; in particular, we show that banks use CDS to obtain capital relief, enhance liquidity and profitability, limit credit supply, and consequently mitigate their NPLs’ level. Moreover, the use of CDS by lenders increases firms’ leverage and value. Leaving a fertile area for debates and future research, the outcomes of this review help provide policy guidance to regulators and financial institutions that make informed decisions to use CDS as a credit risk management instrument.

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“…The Delphi method is a variant of the method of expert assessments, which is characterized by anonymity and controlled feedback (Infante et al 2020). Anonymity is achieved through an individual survey of experts, which does not allow them to discuss answers to questions.…”

Section: Literature Surveymentioning

confidence: 99%

Model of Assessing the Overdue Debts in a Commercial Bank Using Neuro-Fuzzy Technologies

Kovalenko

Afanasieva

Zabuta

et al. 2021

JRFM

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This article considers the problems of overdue credit debt and the creation of effective methods to manage problem debts in banks. The purpose of this paper is to study the problem of overdue credit debt and create effective methods to manage problem debts in financial institutions. Based on a combination of tools of fuzzy logic theory and artificial neural networks, an economic-mathematical model of collection scoring was built. Kohonen self-organizing maps were used to set the parameters of membership functions in the process of fuzzification of quantitative variables of the built model. Data were taken from the official websites of four Bulgarian banks for 2015–2019. The volume of the prepared sample amounted to 1000 credit agreements with active overdue payments. The practical value of the built model of collection scoring for the recovery of overdue debt lies in the possibility to make recommendations for work with each segment of the portfolio of overdue loans in accordance with the calculated level of credit risk. The introduction of credit risk assessment models based on neuro-fuzzy technologies in the work of financial institutions will have a positive impact on the financial results of lending activities of banks.

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Diversifying away risks through derivatives: an analysis of the Italian banking system

Cited by 9 publications

References 41 publications

Learning from Revisions: A Tool for Detecting Potential Errors in Banks' Balance Sheet Statistical Reporting

Learning from Revisions: A Tool for Detecting Potential Errors in Banks' Balance Sheet Statistical Reporting

Why do banks use credit default swaps (CDS)? A systematic review

Model of Assessing the Overdue Debts in a Commercial Bank Using Neuro-Fuzzy Technologies

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