International Monetary Fund. Strategy, Policy, & Review Department
The coverage of risks has become more systematic since the Global Financial Crisis (GFC): staff reports now regularly identify major risks and provide an assessment of their likelihood and economic impact, summarized in Risk Assessment Matrices (RAM). But still limited attention is paid to the range of possible outcomes. Also, risk identification is useful only so much as to inform policy design to preemptively respond to relevant risks and/or better prepare for them. In this regard, policy recommendations in surveillance could be richer in considering various risk management approaches. To this end, progress is needed on two dimensions: • Increasing emphasis on the range of potential outcomes to improve policy design. • Encouraging more proactive policy advice on how to manage risks. Efforts should continue to leverage internal and external resources to support risk analysis and advice in surveillance.
This Technical Assistance Report discusses the findings and recommendations made the IMF mission to assist the Bhutanese authorities in improving estimates of annual GDP, and in developing methods for compiling quarterly GDP estimates. The mission found that the National Statistics Bureau (NSB) is engaged in a range of projects to improve Bhutan’s national accounts statistics. Updated annual GDP statistics signal an improvement in data quality, which should enhance policymakers’ ability to formulate and operationalize evidence-based decisions. Significant improvements to Bhutan’s national accounts statistics can be achieved using a three-step process. The NSB should also keep in mind the need to incorporate methodological and conceptual/definitional revisions during the benchmarking and rebasing process.
This Technical Assistance Report discusses the technical advice and recommendations given by the IMF mission to the authorities of Uganda regarding mapping of source data for savings and credit cooperatives to the IMF’s Standardized Report Form 2SR. The IMF mission’s recommendations are aimed at improving the collection and compilation of monetary statistics for other depository and financial corporations based on the Monetary and Financial Statistics Manual (MFSM). The compilation of monetary statistics and the expansion of its institutional coverage based on the MFSM will improve data quality and usefulness for policy analysis.
Diebold and Yilmaz (2015) recently introduced variance decomposition networks as tools for quantifying and ranking the systemic risk of individual firms. The nature of these networks and their implied rankings depend on the choice decomposition method. The standard choice is the order invariant generalized forecast error variance decomposition of Pesaran and Shin (1998). The shares of the forecast error variation, however, do not add to unity, making difficult to compare risk ratings and risks contributions at two different points in time. As a solution, this paper suggests using the Lanne-Nyberg (2016) decomposition, which shares the order invariance property. To illustrate the differences between both decomposition methods, I analyzed the global financial system during 2001 – 2016. The analysis shows that different decomposition methods yield substantially different systemic risk and vulnerability rankings. This suggests caution is warranted when using rankings and risk contributions for guiding financial regulation and economic policy.
The G-20 Data Gaps Initiative (DGI), which aimed at addressing the information needs
that were revealed by the 2007/2008 global financial crisis, concluded its first phase and
started a second phase (DGI-2) with the endorsement of G-20 Finance Ministers and
Central Bank Governors in September 2015. The DGI-2 recommendations maintain the
continuity of DGI-1 but reflecting the evolving policy needs focus more on datasets that
support the monitoring of risks in the financial sector and the analysis of the inter-linkages
across the economic and financial systems. The paper presents the DGI as an overarching
initiative, bringing together various statistical frameworks for a complete picture of the
economic and financial system to support the work of policy makers.