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引用次数: 0
摘要
本研究利用哥伦比亚贷款增长的每周数据集,采用两步式机器学习方法开发出信贷扩张的每日指标。首先,利用随机森林(RF),使用利差、利率和股市回报率等高频指标填补原始信贷指标中的缺失数据。随后,定量随机森林(Quantile Random Forest)可识别过度信贷创造的时期,尤其是增长率定量超过 95% 的时期,这表明潜在的金融不稳定性。与以往的研究不同,本研究将机器学习与混频分析相结合,创造出一种多功能预警工具,用于识别新兴市场经济体信贷过度增长的情况。这种方法能够处理非线性关系并适应各种不同的情况,为中央银行和宏观审慎监管机构维护金融稳定提供了重要价值。
High frequency monitoring of credit creation: A new tool for central banks in emerging market economies
This study utilizes weekly datasets on loan growth in Colombia to develop a daily indicator of credit expansion using a two-step machine learning approach. Initially, employing Random Forests (RF), missing data in the raw credit indicator is filled using high frequency indicators like spreads, interest rates, and stock market returns. Subsequently, Quantile Random Forest identifies periods of excessive credit creation, particularly focusing on growth quantiles above 95 %, indicative of potential financial instability. Unlike previous studies, this research combines machine learning with mixed frequency analysis to create a versatile early warning instrument for identifying instances of excessive credit growth in emerging market economies. This methodology, with its ability to handle nonlinear relationships and accommodate diverse scenarios, offers significant value to central bankers and macroprudential authorities in safeguarding financial stability.
期刊介绍:
The Quarterly Review of Economics and Finance (QREF) attracts and publishes high quality manuscripts that cover topics in the areas of economics, financial economics and finance. The subject matter may be theoretical, empirical or policy related. Emphasis is placed on quality, originality, clear arguments, persuasive evidence, intelligent analysis and clear writing. At least one Special Issue is published per year. These issues have guest editors, are devoted to a single theme and the papers have well known authors. In addition we pride ourselves in being able to provide three to four article "Focus" sections in most of our issues.