The recent global financial crisis has shown portfolio correlations between agents as one of the major channels of risk contagion and amplification. In this work, we analyse the structure and dynamics of the cross-correlation matrix of banks’ loan portfolios in the yearly bank-firm credit network of Japan during the period from 1980 to 2012. Using the methods of Random Matrix Theory (RMT), Principal Component Analysis and complex networks, we aim to detect non-random patterns in the empirical cross-correlations as well as to identify different states of such correlations over time. Our findings suggest that although a majority of portfolio correlations between banks in lending relations to firms are contributed by noise, the top largest eigenvalues always deviate from the random bulk explained by RMT, indicating the presence of non-random patterns governing the correlation dynamics. In particular, we show that this dynamics is mainly driven by a global common factor and a couple of “groups” factors. Furthermore, different states in the credit market can be identified based on the evolution of eigenvalues and associated eigenvectors. For example, during the asset price bubble period in Japan from 1986 to 1991, we find that banks’ loan portfolios tend to be more correlated, showing a significant increase in the level of systemic risk in the credit market. In addition, building Planar Maximally Filtered Graphs from the correlations of different eigenmodes, notably, we observe that the local interaction structure between banks changes in different periods. Typically, when the dominance of a group of banks in one period gradually vanishes, the credit market starts to build-up a different structure in the next period in which another group of banks will become the main actors in the backbone of the cross-correlations.

最近的全球金融危机表明，代理人之间的投资组合相关性是风险传染和放大的主要渠道之一。在这项工作中，我们分析了 1980 年至 2012 年期间日本银行 - 公司信用网络中银行贷款组合的互相关矩阵的结构和动态。 使用随机矩阵理论（RMT）的方法，在主成分分析和复杂网络中，我们旨在检测经验互相关中的非随机模式，并随着时间的推移识别此类相关的不同状态。我们的研究结果表明，尽管银行与企业贷款关系中的大部分投资组合相关性是由噪声引起的，但最大的特征值总是偏离 RMT 解释的随机体积，表明存在控制相关动态的非随机模式。特别是，我们表明这种动态主要是由一个全局共同因素和几个“群体”因素驱动的。此外，可以根据特征值和相关特征向量的演变来识别信贷市场中的不同状态。例如，在日本 1986 年至 1991 年的资产价格泡沫时期，我们发现银行的贷款组合往往更加相关，表明信贷市场的系统性风险水平显着上升。此外，从不同特征模态的相关性构建平面最大滤波图，值得注意的是，我们观察到银行之间的局部交互结构在不同时期发生了变化。通常，当一组银行在一个时期内的主导地位逐渐消失时，