DATA GAPS AS AN ANCILLARY CULPRIT

The 2007 – 2009 global financial crisis revealed that regulators, supervisors and market players lacked data needed to fully assess risks in financial markets and monitor the financial system. Accordingly, it was observed that “ The crisis has reaffirmed an old lesson – good data and good analysis are the lifeblood of effective surveillance and policy responses both at national and international levels. “ Financial data collected were too aggregated, limited in scope, out of date and incomplete.

To support the development of robust macroprudential frameworks at the national and global levels further work to enhance data for financial stability was needed. This included eff orts to repurpose existing datasets that were already being collected by central banks and prudential supervisors and collecting new data sets where data gaps exist. Despite significant progress, some data gaps remain in place and hinder both the ability of macroprudential authorities to construct and use indicators for systemic risk analysis, implement appropriate macroprudential measures and analyse their effectiveness. Most importantly, it is evident that “ a fully detailed, real-time heat-map of financial system risks is still far out of reach. “

The chapter is structured as follows. Section 1 outlines the post-crisis key initiatives to close data gaps and underlines the remaining data gaps needed to conduct eff ective macroprudential policy. Section 2 explores the types of data needed for macroprudential purposes and suggests key features of data quality in the macroprudential context. Section 3 explores the institutional and governance aspects of data collection in the EU and the UK and critically analyses the unique setting in the US, which separates the macroprudential mandate from the task of data collection and analysis. Section 4 explores two emerging challenges that macroprudential authorities are facing in data collection and analysis. The first is the use of big data and machine learning and the second, the design and use of stress tests to generate data. Section 5 moves away from the particularities of the data collection task of macroprudential authorities and delves deeper into its theoretical foundations. It borrows from the literature in Organisational Studies and in particular, knowledge management and suggests that knowledge and expertise are critical to understanding the role that macroprudential authorities play in the realm of data collection and analysis.