Does a Larger Menu Increase Appetite? Collateral Eligibility and Credit Supply

Bekkum, Sjoerd van; Gabarró, Marc; Irani, Rustom M.

doi:10.1093/rfs/hhx112

Cited by 64 publications

(23 citation statements)

References 29 publications

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“…We study the impact of this policy on the yield differential between green and non-green bonds. While many papers study the effect of accepting a bond as collateral, or "eligibility premium" (Mésonnier, O'Donnell, and Toutain 2017;Van Bekkum, Gabarro, and Irani 2018), here green bonds were not only accepted but also given a preferential status. This policy has, to the best of our knowledge, not been used in other countries.…”

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confidence: 99%

Impact of Green Central Bank Collateral Policy: Evidence from the People’s Bank of China

Macaire¹,

Naef²

2021

Preprint

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In June 2018, the People’s Bank of China (PBoC) decided to include green financial bonds into the pool of assets eligible as collateral for its Medium Term Lending Facility. We measure the impact of the policy on the yield spread between green and non-green bonds, or greenium. Using a difference-in-differences approach to compare pairs of green and non-green bonds issued by the same institutions, we show that the policy increased the greenium by 46 basis points. This experience can be useful to other central banks considering similar polices.

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confidence: 99%

Impact of Green Central Bank Collateral Policy: Evidence from the People’s Bank of China

Macaire¹,

Naef²

2021

Preprint

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“…Finally, we contribute to the debate over whether unconventional ECB policies have undesired effects on bank risk taking via the quality of lending. The VLTROs have the potential to induce risky lending by reducing funding costs (Jiménez et al, 2014), or by leading banks to over-produce collateralizable assets (Van Bekkum et al, 2017;Nyborg, 2017). We find no evidence of low quality VLTRO-induced lending.…”

Section: Related Literaturementioning

confidence: 49%

Unconventional Monetary Policy and Bank Lending Relationships

2017

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We explore how banks transmit central bank liquidity injections using unique variation in the ECB's 2011-12 Very Long-Term Refinancing Operations (VLTROs) which affected lending to firms discontinuously across credit ratings (i.e., within banks). We show that banks transmit liquidity differently to multi-bank firms than to firms with only one bank. Single-bank firms receive longer-term relationship lending and increase investment, while multi-bank firms receive short-term transactions-style lending only.Policy effects are attributable to increasing the maturity of bank borrowing from the ECB in combination with allowing banks to use loans to firms as collateral for such borrowing.

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“…For instance, mortgage data for the USA are provided by the Federal National Mortgage Association (Fannie Mae) 7 and by the Federal Home Loan Mortgage Corporation (Freddie Mac): 8 they report loan-level information for millions of individual mortgages, with numerous associated features, e.g., repayment status, borrower's main characteristics, and granting location of the loan (we refer to [2,35] for two examples of mortgage-level analysis in the US). A similar level of detail is found in the European Datawarehouse, 9 which provides loan-level data of European assets about residential mortgages, credit cards, car leasing, and consumer finance (see [20,40] for two examples of economic analysis on such data).…”

Section: Stewardship and Protectionmentioning

confidence: 86%

Data Science Technologies in Economics and Finance: A Gentle Walk-In

Barbaglia

Consoli

Manzan

et al. 2021

Data Science for Economics and Finance

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This chapter is an introduction to the use of data science technologies in the fields of economics and finance. The recent explosion in computation and information technology in the past decade has made available vast amounts of data in various domains, which has been referred to as Big Data. In economics and finance, in particular, tapping into these data brings research and business closer together, as data generated in ordinary economic activity can be used towards effective and personalized models. In this context, the recent use of data science technologies for economics and finance provides mutual benefits to both scientists and professionals, improving forecasting and nowcasting for several kinds of applications. This chapter introduces the subject through underlying technical challenges such as data handling and protection, modeling, integration, and interpretation. It also outlines some of the common issues in economic modeling with data science technologies and surveys the relevant big data management and analytics solutions, motivating the use of data science methods in economics and finance.

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Does a Larger Menu Increase Appetite? Collateral Eligibility and Credit Supply

Cited by 64 publications

References 29 publications

Impact of Green Central Bank Collateral Policy: Evidence from the People’s Bank of China

Impact of Green Central Bank Collateral Policy: Evidence from the People’s Bank of China

Unconventional Monetary Policy and Bank Lending Relationships

Data Science Technologies in Economics and Finance: A Gentle Walk-In

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