Several central banks have begun actively investigating the possibility of issuing central bank digital currency (CBDC). This new central bank liability would be a widely accessible digital form of fiat money, intended as legal tender. This chapter aims to answer a simple question: Does CBDC offer benefits? On the demand side, would it satisfy end user needs better than other forms of money? And on the supply side, would issuing CBDC allow central banks to more effectively satisfy public policy goals, including financial inclusion, operational efficiency, financial stability, monetary policy effectiveness, and financial integrity? In short, is CBDC a desirable form of money given existing and rapidly evolving alternatives? The chapter includes a summary of pilot projects and studies from central banks exploring the possibility of issuing CBDC. The analysis is based on publicly issued materials and discussions with staff members at central banks and technology providers around the world.

DISCLAIMER: Staff Discussion Notes (SDNs) showcase policy-related analysis and research being developed by IMF staff members and are published to elicit comments and to encourage debate. The views expressed in Staff Discussion Notes are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

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Rating collateralised debt obligations (CDOs), which are based on tranched pools of credit risk exposures, does not only require attributing a probability of default to each obligor within the portfolio. It also involves assumptions concerning recovery rates and correlated defaults of pool assets, thus combining credit risk assessments of individual collateral assets with estimates about default correlations and other modelling assumptions. In this paper, we explain one of the most well-known models for rating CDOs, the so-called binomial expansion technique (BET). Comparing this approach with an alternative methodology based on Monte Carlo simulation, we then highlight the potential importance of correlation assumptions for the ratings of senior CDO tranches and explore what differences in methodologies across rating agencies may mean for senior tranche rating outcomes. The remainder of the paper talks about potential implications of certain model assumptions for ratings accuracy, that is the "model risk" taken by investors when acquiring CDO tranches, and whether and under what conditions methodological differences may generate incentives for issuers to strategically select rating agencies to get particular CDO structures rated.

Credit rating agencies face a difficult trade-off between delivering both accurate and stable ratings. In particular, its users have consistently expressed a preference for rating stability, driven by the transactions costs induced by trading when ratings change frequently. Rating agencies generally assign ratings on a through-the-cycle basis whereas banks' internal valuations are often based on a point-in-time performance, that is they are related to the current value of the rated entity's or instrument's underlying assets. This paper compares the two approaches and assesses their impact on rating stability and accuracy. We find that while through-the-cycle ratings are initially more stable, they are prone to rating cliff effects and also suffer from inferior performance in predicting future defaults. This is because they are typically smooth and delay rating changes. Using a through-the-crisis methodology that uses a more stringent stress test goes halfway toward mitigating cliff effects, but is still prone to discretionary rating change delays.