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Title: Monitoring financial stress spillovers with high-frequency principal components Authors:  Massimiliano Caporin - University of Padova (Italy)
Laura Garcia-Jorcano - Universidad de Castilla-La Mancha (Spain) [presenting]
Juan-Angel Jimenez-Martin - Complutense of Madrid (Spain)
Abstract: High-frequency principal components (HF PC) are used to extract information from stock prices to monitor and measure the existing level of stress in the financial system, which can be named the level of systemic stress, which is the amount of systemic risk that has already materialized. The empirical analysis using one-minute returns of stocks included in the Russel 3000 index from 2003 to 2021 shows that there exists a clear relationship between realized eigenvalues and systemic increases in financial stress. We also find that realized eigenvectors can trace the role of firms/sectors as potential sources of financial stress in different periods of time. Then, we measure the transmission of shocks from (to) the financial sector to (from) other non-financial sectors and the real economy. This provides a tool to analyze the spread of this financial instability that could affect the functioning of the financial system to the point where the real economy is seriously damaged. HF PC can be interpreted as a risk identification framework that allows policymakers and central banks to detect risks in good time and address potential threats to financial stability with the most appropriate policy tools.