信用风险的宏观经济决定因素——以不良贷款为例

IF 0.5 Q4 ECONOMICS
Adam Zawadzki
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引用次数: 0

摘要

摘要本文的主要目的是考察影响信用风险的主要宏观经济因素,即不良贷款率(以下简称不良贷款率)。根据结果,国内信贷对私营部门的比率,经济合作与发展组织(OECD)成员国与不良贷款呈负相关关系,而失业率和公共债务比率与不良贷款呈正相关关系,具有统计学意义。此外,还证明了通货膨胀率与本国货币贬值之间的相关性。这项研究考察了引发金融危机的2008年信贷紧缩的影响。样本包括2009-2019年期间的106个国家。实际GDP增长、失业率、公共债务比率、国内信贷与私营部门比率、货币贬值、通货膨胀率和利率作为宏观经济因素进行了分析。分析中包含了一个代表经合组织成员资格的虚拟变量。采用普通最小二乘法(OLS)进行估计。本文对不良贷款面板数据视角的学术论述有所贡献,同时对各国政府和国际投资者具有借鉴意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Macroeconomic Determinants of Credit Risk on the Example of Non-performing Loans
Abstract The primary goal of this article is to examine the principal macroeconomic factors influencing credit risk as assessed by the nonperforming loan ratio (hereinafter NPL ratio). Based on the results, the ratio of domestic credit to the private sector, Organization for Economic Cooperation and Development (OECD) membership with a negative correlation with NPLs while the unemployment rate and the ratio of public debt with a positive relation with NPLs were statistically significant. In addition, the correlation between the inflation rate and the depreciation of the home currency was proven. The research examines the effects of the 2008 credit crunch, which triggered the financial crisis. The sample comprises 106 countries for the period 2009–2019. The real GDP growth, unemployment rate, public debt ratio, domestic credit to private sector ratio, currency depreciation, inflation rate, and interest rate were analysed as macroeconomic factors. A dummy variable representing OECD membership has been included in the analysis. The estimations were performed using the ordinary least squares (OLS) method. This article contributes to the academic discourse on the panel data perspective with regard to non-performing loans, while the practical implications are beneficial for governments and international investors.
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来源期刊
CiteScore
0.60
自引率
0.00%
发文量
9
期刊介绍: The Central European Journal of Economic Modelling and Econometrics (CEJEME) is a quarterly international journal. It aims to publish articles focusing on mathematical or statistical models in economic sciences. Papers covering the application of existing econometric techniques to a wide variety of problems in economics, in particular in macroeconomics and finance are welcome. Advanced empirical studies devoted to modelling and forecasting of Central and Eastern European economies are of particular interest. Any rigorous methods of statistical inference can be used and articles representing Bayesian econometrics are decidedly within the range of the Journal''s interests.
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