Jose M Garrido, Ms. Chanda M DeLong, Amira Rasekh, and Anjum Rosha
The Directive on Restructuring and Insolvency sets minimum standards for restructuring and certain insolvency matters, but its harmonization effect will be limited given multiple options for implementation, likely leading to divergent restructuring models in Europe. These options reveal different policy approaches to the regulation of restructuring and insolvency. The analysis in this paper aims to illustrate the breadth of the policy choices and their consequences for restructuring activity. States should carefully design restructuring procedures to avoid the negative economic effects of certain options that could undermine creditors’ rights or result in unpredictable outcomes, particularly in cross-border cases.
Sweden entered the pandemic with substantial buffers and suffered a relatively shallow recession in 2020. The decline in output was moderated by substantial income and liquidity support as well as structural features of the economy. Sweden’s initial less stringent containment strategy seems to have altered the timing of the economic fallout, which intensified towards the middle of the year. This fallout has particularly impacted the youth and foreign-born. Economic recovery is projected over the next two years, but uncertainty has increased due to the new strains of the virus and slow vaccination.
Hans Weisfeld, Mr. Irineu E de Carvalho Filho, Mr. Fabio Comelli, Rahul Giri, Klaus-Peter Hellwig, Chengyu Huang, Fei Liu, Mrs. Sandra V Lizarazo Ruiz, Alexis Mayer Cirkel, and Mr. Andrea F Presbitero
In recent years, Fund staff has prepared cross-country analyses of macroeconomic vulnerabilities in low-income countries, focusing on the risk of sharp declines in economic growth and of debt distress. We discuss routes to broadening this focus by adding several macroeconomic and macrofinancial vulnerability concepts. The associated early warning systems draw on advances in predictive modeling.
We introduce unFEAR, Unsupervised Feature Extraction Clustering, to identify economic crisis regimes. Given labeled crisis and non-crisis episodes and the corresponding features values, unFEAR uses unsupervised representation learning and a novel mode contrastive autoencoder to group episodes into time-invariant non-overlapping clusters, each of which could be identified with a different regime. The likelihood that a country may experience an econmic crisis could be set equal to its cluster crisis frequency. Moreover, unFEAR could serve as a first step towards developing cluster-specific crisis prediction models tailored to each crisis regime.