Correlation structure of the Spanish stock market around COVID-19 using random matrix theory

In this work we analyze the correlation structure of the Spanish stock market around COVID-19 using random matrix theory (RMT). The results reveal that the empirical spectral distribution of eigenvalues associated with correlation matrices of prices for major companies listed on IBEX35 and IBEXC differs across the analyzed periods. In all cases, it deviates from the theoretical spectral distribution predicted by RMT through the Marchenko-Pastur law. In particular, during the COVID-19 crisis, the maximum eigenvalue exceeds the maximum eigenvalue in different periods before and after the pandemic, effectively capturing the state or mode of the market under crisis conditions. The second-largest eigenvalue facilitates the identification of groups among stocks associated with its corresponding eigenvector. These findings hold potential for developing strategies to assess systemic risk in the Spanish stock market.

​In this work we analyze the correlation structure of the Spanish stock market around COVID-19 using random matrix theory (RMT). The results reveal that the empirical spectral distribution of eigenvalues associated with correlation matrices of prices for major companies listed on IBEX35 and IBEXC differs across the analyzed periods. In all cases, it deviates from the theoretical spectral distribution predicted by RMT through the Marchenko-Pastur law. In particular, during the COVID-19 crisis, the maximum eigenvalue exceeds the maximum eigenvalue in different periods before and after the pandemic, effectively capturing the state or mode of the market under crisis conditions. The second-largest eigenvalue facilitates the identification of groups among stocks associated with its corresponding eigenvector. These findings hold potential for developing strategies to assess systemic risk in the Spanish stock market. Read More

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