Federal Reserve Bank of San Francisco
Working Paper Series
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.
Cite this item
Galina Hale & Jose A. Lopez, Monitoring Banking System Fragility with Big Data, Federal Reserve Bank of San Francisco, Working Paper Series 2018-1, 15 Sep 2017, revised 23 Apr 2018.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
- G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
- G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
This item with handle RePEc:fip:fedfwp:2018-01
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