2018-01 | January 2018
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Monitoring Banking System Fragility with Big Data
The need to monitor aggregate financial stability was made clear during the global financial crisis of 2008-2009, and, of course, the need to monitor individual financial firms from a microprudential standpoint remains. In this paper, we propose a procedure based on mixed-frequency models and network analysis to help address both of these policy concerns. We decompose firm-specific stock returns into two components: one that is explained by observed covariates (or fitted values), the other unexplained (or residuals). We construct networks based on the co-movement of these components. Analysis of these networks allows us to identify time periods of increased risk concentration in the banking sector and determine which firms pose high systemic risk. Our results illustrate the efficacy of such modeling techniques for monitoring and potentially enhancing national financial stability.
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Hale, Galina, and Jose A. Lopez. 2018. "Monitoring Banking System Fragility with Big Data ," Federal Reserve Bank of San Francisco Working Paper 2018-01. Available at https://doi.org/10.24148/wp2018-01