Abstract The recent financial crisis has brought to the fore issues of quantifying and reducing systemic risk. This focus has precipitated the exploration of various methods for measuring systemic risks and for attributing systemic risk contributions to systemically important financial institutions. Concomitant with this stream of research are efforts to collect, standardize and store data useful to these modeling efforts. While discussions of modeling approaches are pervasive in the literature on systemic risk, issues of data requirements and suitability are often relegated 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 models are feasible given the available data; conversely, the mapping 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 review 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.

Introduction