Aligning Models and Data for Systemic Risk Analysis

Aligning Models and Data for Systemic Risk Analysis

Stein, R. M., 2013.  in The Handbook of Systemic Risk. Oxford University Press. pp. 37-65.

 

The recent financial crisis has brought to the fore issues of understanding and reducing systemic risk. This focus has precipitated exploration of various methods for measuring systemic risks and for attributing systemic risk contribu- tions to systemically important financial institutions. Concomitant with this stream of research are efforts to collect, standardize and store data useful to these mod- eling efforts. While discussions of modeling approaches are pervasive in the liter- ature on systemic risk, issues of data requirements and suitability are often rele- gated to the status of implementation details. This short chapter is an attempt to deepen this discussion. We provide a 2 × 2 mapping of modeling strategies to key data characteristics and constraints that can help modelers determine which mod- els are feasible given the available data; conversely, it can provide guidance for data collection efforts in cases where specific analytic properties are desired. The framework may also be useful for evaluating, at a conceptual level, the trade-offs for incremental data collection. To provide background for this mapping, we re- view the analytic benefits and limitations of using aggregate vs. micro-level data, provide background on the role of data linking and discuss some of the practical aspects of data pooling including concerns about confidentiality. Throughout the chapter, we include examples from various domains to make the points we outline concrete.

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Aggregate and firm-level measures of systemic risk from a structural model of default

Aggregate and firm-level measures of systemic risk from a structural model of default

Reyngold, A., K. Shnyra. and R. M. Stein. 2013. MIT LFE Working Paper LFE-0501-13.

 

The breadth and dynamics of the recent financial crisis have led to efforts to develop forward-looking tools to monitor systemic risk. In this paper we propose a new measure which is an extension of the absorption ratio (AR) introduced in Kritzman et al. (2010). Using principal component analysis (as in the original AR methodology) in conjunction with a structural model of default we develop a measure of systemic risk that may be calculated using only publicly available data. We call our new measure the credit absorption ratio (CAR). We find that increases in the CAR preceded periods of financial distress during the recent crisis. The CAR may be interpreted economically: it highlights states of the financial system during which the credit fundamentals of institutions and markets exhibit heightened coupling and higher potential for cascading distress. We also demonstrate that a byproduct of CAR analysis provides a measure of the degree to which specific financial institutions are exposed to systemic risk factors at any point in time. We find that a number of the institutions that exhibited high potential exposure under our measure during the lead-up to the recent crisis subsequently experienced higher levels of distress or required external assistance.

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