Portfolio Correlations in the Bank-Firm Credit Market of Japan
54 Pages Posted: 16 Nov 2018 Last revised: 10 Feb 2019
Date Written: February 2019
The recent global financial crisis has shown portfolio correlations between agents as one of the key 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. Employing the methods of Random Matrix Theory (RMT), Principal Component Analysis (PCA) 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 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 patterns governing such correlations. In particular, the correlation dynamics are mainly driven by a global common factor and a couple of "groups" factors.
Furthermore, different states in the market can be identified based on the analysis of 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 dynamically Planar Maximally Filtered Graphs (PMFG) from the correlations of different eigenmodes, notably, we observe that the local interaction structure between banks changes in different periods. In particular, typically when the dominance of a group of banks in one period gradually disappears, the credit market starts to build-up a different structure in the next period in which another group of banks will dominate in the backbone of the cross-correlations.
Keywords: Bank Lending, Portfolio Correlations, Systemic Risk, Random Matrix Theory, Principal Component Analysis, Correlation-Based Filtered Networks
JEL Classification: C13, G11, G21, G32
Suggested Citation: Suggested Citation