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Assaf Razin and Efraim Sadka

. Conclusion Our simple credit-rating model captures key features of the Brazil-type debt crisis. Its applicability to Brazil lies in two common features. (1) Both in the model and in the case of Brazil, macro fundamentals are not shaky (e.g., the primary surplus in Brazil in the wake of the crisis was about 2.25 percent of the GNP). (2) Both in the model and in the Brazilian case, the “coordinator” of market expectations that shift the market outcome is extraneous to the market economy. In Brazil the expectations coordinator appears to have been forthcoming

Mr. Luis Catão and Mr. Gian M Milesi-Ferretti
We examine the determinants of external crises, focusing on the role of foreign liabilities and their composition. Using a variety of statistical tools and comprehensive data spanning 1970-2011, we find that the ratio of net foreign liabilities (NFL) to GDP is a significant crisis predictor, and the more so when it exceeds 50 percent in absolute terms and 20 percent of the country-specific historical mean. This is primarily due to net external debt--the effect of net equity liabilities is weaker and net FDI liabilities seem if anything an offset factor. We also find that: i) breaking down net external debt into its gross asset and liability counterparts does not add significant explanatory power to crisis prediction; ii) the current account is a powerful predictor, either measured unconditionally or as deviations from conventionally estimated “norms” iii) foreign exchange reserves reduce the likelihood of crisis more than other foreign asset holdings; iv) a parsimonious probit containing those and a handful of other variables has good predictive performance in- and out-of-sample. The latter result stems largely from our focus on external crises stricto sensu.
Mr. Paul H. Kupiec
This paper considers characteristics of the capital requirements proposed in The New Basel Capital Accord (2001). Formal analysis identifies calibration features that could give rise to unintended consequences that may include: concentration of credit risk in institutions that are less well equipped to measure and manage risks; an overabundance of thinly capitalized high quality long-maturity credits in foundation Internal Ratings-Based (IRB) banks; distortions in the secondary market for discount or premium credits; an increase in the difficulty of resolving distressed financial institutions; and incentives to distort the accuracy of loan loss provisions.
International Monetary Fund
The present financial crisis is testing the resilience of the global financial system as well as the robustness of national and multilateral policy frameworks. As requested by Executive Directors, this paper reviews recent progress in meeting these challenges, focusing on the role of the Fund and its collaboration with the Financial Stability Forum (FSF). In concert with other international bodies, the Fund has sought to promote appropriate policy responses to the financial turmoil, including through its report on The Recent Financial Turmoil—Initial Assessment, Policy Lessons, and Implications for Fund Surveillance, in the Global Financial Stability Report (GFSR) and the World Economic Outlook (WEO), as well as in recent Article IV consultations and Financial Sector Assessment Programs (FSAPs). The Fund has also responded to the International Monetary and Financial Committee’s (IMFC) call for closer collaboration with other international fora, including by supporting the implementation of policy lessons from the crisis, such as the 67 FSF recommendations issued in April 2008.
International Monetary Fund. Monetary and Capital Markets Department
This paper first takes a historical perspective, studying the implications of the oil boom of the 2000s on industry structure and economy-wide productivity. It then examines progress with the ongoing transition thus far both in the real sector and in the labor market, bearing in mind the short time span that has passed. This paper also explores two possible explanations for lagging productivity—namely, product market regulation and the low level of research and innovation. An extensive data set of mainland Norwegian firms is used to empirically assess the potential productivity gains from product market reforms as well as increasing research and development spending.
International Monetary Fund
This paper develops indices of fiscal transparency for a broad range of countries based on the IMF's Code of Good Practices on Fiscal Transparency, using data derived from published fiscal transparency modules of the Reports on the Observance of Standards and Codes (ROSCs). The indices covers four clusters of fiscal transparency practices: data assurances, medium-term budgeting, budget execution reporting, and fiscal risk disclosures. More transparent countries are shown to have better credit ratings, better fiscal discipline, and less corruption, after controlling for other socioeconomic variables.
International Monetary Fund. Monetary and Capital Markets Department
This virtual technical assistance (TA) mission supported the Agency in strengthening certain elements of its risk based supervisory framework. The mission focused on assisting the Agency with its development of internal supervisory methodologies for assessing a bank’s ICAAP, and for setting individual Pillar 2 supervisory capital requirements. The mission provided recommendations and targeted training. The priorities for the next TA missions were discussed with the Agency (strengthening banking supervision and cybersecurity, and diagnostic TA of insurance sector supervision will be considered). The mission benefited from simultaneous translation.
Majid Bazarbash
Recent advances in digital technology and big data have allowed FinTech (financial technology) lending to emerge as a potentially promising solution to reduce the cost of credit and increase financial inclusion. However, machine learning (ML) methods that lie at the heart of FinTech credit have remained largely a black box for the nontechnical audience. This paper contributes to the literature by discussing potential strengths and weaknesses of ML-based credit assessment through (1) presenting core ideas and the most common techniques in ML for the nontechnical audience; and (2) discussing the fundamental challenges in credit risk analysis. FinTech credit has the potential to enhance financial inclusion and outperform traditional credit scoring by (1) leveraging nontraditional data sources to improve the assessment of the borrower’s track record; (2) appraising collateral value; (3) forecasting income prospects; and (4) predicting changes in general conditions. However, because of the central role of data in ML-based analysis, data relevance should be ensured, especially in situations when a deep structural change occurs, when borrowers could counterfeit certain indicators, and when agency problems arising from information asymmetry could not be resolved. To avoid digital financial exclusion and redlining, variables that trigger discrimination should not be used to assess credit rating.