{"title":"论重组时期银行业盈利能力的驱动因素:贝叶斯视角","authors":"Paula Cruz-García, A. Forte, Jesús Peiró‐Palomino","doi":"10.1108/aea-01-2020-0003","DOIUrl":null,"url":null,"abstract":"\nPurpose\nThere is abundant literature analyzing the determinants of banks’ profitability through its main component: the net interest margin. Some of these determinants are suggested by seminal theoretical models and subsequent expansions. Others are ad-hoc selections. Up to now, there are no studies assessing these models from a Bayesian model uncertainty perspective. This paper aims to analyze this issue for the EU-15 countries for the period 2008-2014, which mainly corresponds to the Great Recession years.\n\n\nDesign/methodology/approach\nIt follows a Bayesian variable selection approach to analyze, in a first step, which variables of those suggested by the literature are actually good predictors of banks’ net interest margin. In a second step, using a model selection approach, the authors select the model with the best fit. Finally, the paper provides inference and quantifies the economic impact of the variables selected as good candidates.\n\n\nFindings\nThe results widely support the validity of the determinants proposed by the seminal models, with only minor discrepancies, reinforcing their capacity to explain net interest margin disparities also during the recent period of restructuring of the banking industry.\n\n\nOriginality/value\nThe paper is, to the best of the knowledge, the first one following a Bayesian variable selection approach in this field of the literature.\n","PeriodicalId":36191,"journal":{"name":"Applied Economic Analysis","volume":" ","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2020-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1108/aea-01-2020-0003","citationCount":"5","resultStr":"{\"title\":\"On the drivers of profitability in the banking industry in restructuring times: a Bayesian perspective\",\"authors\":\"Paula Cruz-García, A. Forte, Jesús Peiró‐Palomino\",\"doi\":\"10.1108/aea-01-2020-0003\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\nPurpose\\nThere is abundant literature analyzing the determinants of banks’ profitability through its main component: the net interest margin. Some of these determinants are suggested by seminal theoretical models and subsequent expansions. Others are ad-hoc selections. Up to now, there are no studies assessing these models from a Bayesian model uncertainty perspective. This paper aims to analyze this issue for the EU-15 countries for the period 2008-2014, which mainly corresponds to the Great Recession years.\\n\\n\\nDesign/methodology/approach\\nIt follows a Bayesian variable selection approach to analyze, in a first step, which variables of those suggested by the literature are actually good predictors of banks’ net interest margin. In a second step, using a model selection approach, the authors select the model with the best fit. Finally, the paper provides inference and quantifies the economic impact of the variables selected as good candidates.\\n\\n\\nFindings\\nThe results widely support the validity of the determinants proposed by the seminal models, with only minor discrepancies, reinforcing their capacity to explain net interest margin disparities also during the recent period of restructuring of the banking industry.\\n\\n\\nOriginality/value\\nThe paper is, to the best of the knowledge, the first one following a Bayesian variable selection approach in this field of the literature.\\n\",\"PeriodicalId\":36191,\"journal\":{\"name\":\"Applied Economic Analysis\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2020-06-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1108/aea-01-2020-0003\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Economic Analysis\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://doi.org/10.1108/aea-01-2020-0003\",\"RegionNum\":3,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Economic Analysis","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.1108/aea-01-2020-0003","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ECONOMICS","Score":null,"Total":0}
On the drivers of profitability in the banking industry in restructuring times: a Bayesian perspective
Purpose
There is abundant literature analyzing the determinants of banks’ profitability through its main component: the net interest margin. Some of these determinants are suggested by seminal theoretical models and subsequent expansions. Others are ad-hoc selections. Up to now, there are no studies assessing these models from a Bayesian model uncertainty perspective. This paper aims to analyze this issue for the EU-15 countries for the period 2008-2014, which mainly corresponds to the Great Recession years.
Design/methodology/approach
It follows a Bayesian variable selection approach to analyze, in a first step, which variables of those suggested by the literature are actually good predictors of banks’ net interest margin. In a second step, using a model selection approach, the authors select the model with the best fit. Finally, the paper provides inference and quantifies the economic impact of the variables selected as good candidates.
Findings
The results widely support the validity of the determinants proposed by the seminal models, with only minor discrepancies, reinforcing their capacity to explain net interest margin disparities also during the recent period of restructuring of the banking industry.
Originality/value
The paper is, to the best of the knowledge, the first one following a Bayesian variable selection approach in this field of the literature.