ABSTRACT

The paper deals with the identification of time-frequency regions describing cyclicality of bank loans before, during and after the 2008 crisis via wavelets. We bring new methods and findings about the short and medium cycles of loans provided to corporates and households in the Euro Area in 2000–2017 using seasonally unadjusted monthly data. We have recognized an impact of the crisis on data volatility which further influences the type of significance testing of wavelet spectrograms. To avoid this influence we propose: (1) an adaptive spectrogram testing based on Torrence and Compo approach and (2) robustness analysis via enhanced spectrogram modelling tested by the MC simulations. Both cross-checked approaches prove the sensitivity of standard wavelet tests on data volatility. The results confirm the usability of the new approaches and show that the crisis in 2008 influenced the cyclical behaviour of both categories of economic sectors, but in a different way.

摘要

本文通过小波来识别描述银行贷款周期性的时频区域，该时间频率区域描述了2008年危机之前，之中和之后。我们使用未经季节性调整的月度数据，介绍了2000-2017年向欧元区公司和家庭提供的短期和中期贷款的新方法和发现。我们已经认识到危机对数据波动性的影响，这进一步影响了小波频谱图的重要性测试的类型。为了避免这种影响，我们建议：（1）基于Torrence和Compo方法的自适应频谱图测试，以及（2）通过由MC仿真测试的增强频谱图建模进行的鲁棒性分析。两种交叉检验的方法都证明了标准小波检验对数据波动性的敏感性。