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The Effects of Interest Rates and Macroprudential Policies on Banks'...
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The Effects of Interest Rates and Macroprudential Policies on Banks' Credit Portfolio

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074).

Deadline for manuscript submissions: 31 August 2024 | Viewed by 6431

Special Issue Editors

Prof. Dr. Daniel Oliveira Cajueiro

E-Mail Website
Guest Editor
Department of Economics, Universidade de Brasília, Brasilia, Brazil
Interests: finance and banking; blockchains; consensus algorithms; DeFi and FinTechs; artificial intelligence; natural language processing; machine learning and data science; control and optimization theory; complex systems; scientific computation and real web-based applications with commercial value; operations research; time-series forecasting; agent-based models
Dr. Regis A. Ely

E-Mail Website
Guest Editor
Departamento de Economia, Universidade Federal de Pelotas, Pelotas, Brazil
Interests: finance; econometrics; economics

Special Issue Information

Dear Colleagues,

It is our understanding that banks' balance sheets are strongly affected by interest rates and other credit and macroprudential policies implemented by monetary authorities. In this Special Issue, we are interested in how the establishment of interest rates and macroprudential regulations affects the profit and risk profile of banks, as well as the composition of credit and the supply and demand for credit in the real sector of the economy.

We welcome interdisciplinary contributions that include both theoretical and empirical approaches from researchers and practitioners. We do not want to constrain ourselves to these approaches, but we especially want to call attention to the relevance of causal inference and financial econometrics, game and microeconomic theory, agent-based models and other computational models, complex network models, risk modelling, and machine learning and artificial intelligence approaches.

Prof. Dr. Daniel Oliveira Cajueiro
Dr. Regis A. Ely
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Risk and Financial Management is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • interest rate
  • monetary policy
  • macroprudential policies
  • credit portfolio
  • risk
  • profit

Published Papers (2 papers)

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Research

35 pages, 1199 KiB  
Article
Expansionary Monetary Policy and Bank Loan Loss Provisioning
by Mengyang Guo, Xiaoran Jia, Justin Yiqiang Jin, Kiridaran Kanagaretnam and Gerald J. Lobo
J. Risk Financial Manag. 2024, 17(1), 8; https://doi.org/10.3390/jrfm17010008 - 22 Dec 2023
Viewed by 1639
Abstract
We explore how expansionary monetary policy (EMP) influences bank loan loss provisioning. We find that banks’ discretionary loan loss provisions (DLLPs) increase during periods of EMP. This effect is stronger for banks with greater risk-taking, a larger proportion of influential stakeholders, lower ex-ante [...] Read more.
We explore how expansionary monetary policy (EMP) influences bank loan loss provisioning. We find that banks’ discretionary loan loss provisions (DLLPs) increase during periods of EMP. This effect is stronger for banks with greater risk-taking, a larger proportion of influential stakeholders, lower ex-ante transparency of loan loss provisions, and more stringent bank regulation, which is consistent with external stakeholders requiring more conservative and timelier loan loss provisioning. We also find that both the timeliness and the validity of banks’ loan loss provisions (LLPs) increase during EMP periods. Our results are robust to the use of instrumental variable estimation and exogenous variations in monetary policy. Lastly, we show that conservative (i.e., higher DLLPs) and timely loan loss provisioning discipline banks from excessive risk-taking during periods of EMP. Full article
(This article belongs to the Special Issue The Effects of Interest Rates and Macroprudential Policies on Banks' Credit Portfolio)
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21 pages, 3167 KiB  
Article
Machine Learning for Enhanced Credit Risk Assessment: An Empirical Approach
by Nicolas Suhadolnik, Jo Ueyama and Sergio Da Silva
J. Risk Financial Manag. 2023, 16(12), 496; https://doi.org/10.3390/jrfm16120496 - 27 Nov 2023
Cited by 3 | Viewed by 4299
Abstract
Financial institutions and regulators increasingly rely on large-scale data analysis, particularly machine learning, for credit decisions. This paper assesses ten machine learning algorithms using a dataset of over 2.5 million observations from a financial institution. We also summarize key statistical and machine learning [...] Read more.
Financial institutions and regulators increasingly rely on large-scale data analysis, particularly machine learning, for credit decisions. This paper assesses ten machine learning algorithms using a dataset of over 2.5 million observations from a financial institution. We also summarize key statistical and machine learning models in credit scoring and review current research findings. Our results indicate that ensemble models, particularly XGBoost, outperform traditional algorithms such as logistic regression in credit classification. Researchers and experts in the subject of credit risk can use this work as a practical reference as it covers crucial phases of data processing, exploratory data analysis, modeling, and evaluation metrics. Full article
(This article belongs to the Special Issue The Effects of Interest Rates and Macroprudential Policies on Banks' Credit Portfolio)
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