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International Monetary Fund. Strategy, Policy, & Review Department

horse-race format . The models assess the near-term risk of a crisis in the external, financial, fiscal, and real sectors. In each sector, rigorous performance metrics are used to compare new tools against traditional approaches. It turns out that random forest-based models, which are popular modern ML methods that average over many decision trees, outperform other options in most cases. In other cases, the signal extraction approach, a robust non-parametric method designed for macro-crisis detection, performs best. These winning models represent a new generation of

Mr. Kasper Lund-Jensen

Front Matter Page Monetary and Capital Markets Department Contents I. Introduction II. Related Literature III. Econometric Methodology and Model Specification A. Model Specification IV. Estimation Results V. Monitoring Systemic Risk A. The Signal Extraction Approach B. Crisis signals based on binary response model C. Risk Factor Thresholds D. Out-of-Sample Analysis VI. Concluding Remarks References Tables 1. Countries in Data Sample 2. Systemic Risk Factors based on Dynamic Logit Model, 1970–2010 3. Signal

Mr. Kasper Lund-Jensen
Successful implementation of macroprudential policy is contingent on the ability to identify and estimate systemic risk in real time. In this paper, systemic risk is defined as the conditional probability of a systemic banking crisis and this conditional probability is modeled in a fixed effect binary response model framework. The model structure is dynamic and is designed for monitoring as the systemic risk forecasts only depend on data that are available in real time. Several risk factors are identified and it is hereby shown that the level of systemic risk contains a predictable component which varies through time. Furthermore, it is shown how the systemic risk forecasts map into crisis signals and how policy thresholds are derived in this framework. Finally, in an out-of-sample exercise, it is shown that the systemic risk estimates provided reliable early warning signals ahead of the recent financial crisis for several economies.
Mr. Kasper Lund-Jensen

relative to static thresholds based on the signal extraction approach. 4 Finally, I perform a pseudo out-of-sample analysis for the period 2001-2010 in order to assess whether the risk factors provided early-warning signals ahead of the recent financial crisis. Based on the empirical analysis, I reach the following main conclusions: Systemic risk, as defined here, does appear to be predictable in real time. In particular, the following risk factors are identified: banking sector leverage, equity price growth, the credit-to-GDP gap, real effective exchange rate

International Monetary Fund. Strategy, Policy, & Review Department

Forecasting 8 Early Warning Systems (EWSs) have long been a common feature in country surveillance . Both private- and public-sector institutions have repeatedly emphasized the development of models to anticipate crises, especially in the wake of the emerging-market turbulence of the 1990s. The traditional early-warning literature has typically relied on two approaches—discrete-choice (logit or probit) regressions (see, for example, Eichengreen and Rose, 1998 ) or the signal extraction approach pioneered for the Fund by Kaminsky and Reinhart (1999) —the latter has

Mr. Ashvin Ahuja, Kevin Wiseman, and Mr. Murtaza H Syed

Extraction Model EM- and LIC-specifc risk assessments discussed in this note are based on a common signal extraction approach. 1 The model assesses vulnerability to a crisis by establishing thresholds for key indicators in the data, and aggregating over the indicators which exceed their thresholds. The defnition of a crisis in the data and the indicators that are most informative for that type of crisis are different for each use of the model. The sudden stop model used for some EMs, for example, naturally defnes crises in terms of capital fows and emphasizes external

International Monetary Fund. Strategy, Policy, & Review Department
The IMF’s Vulnerability Exercise (VE) is a cross-country exercise that identifies country-specific near-term macroeconomic risks. As a key element of the Fund’s broader risk architecture, the VE is a bottom-up, multi-sectoral approach to risk assessments for all IMF member countries. The VE modeling toolkit is regularly updated in response to global economic developments and the latest modeling innovations. The new generation of VE models presented here leverages machine-learning algorithms. The models can better capture interactions between different parts of the economy and non-linear relationships that are not well measured in ”normal times.” The performance of machine-learning-based models is evaluated against more conventional models in a horse-race format. The paper also presents direct, transparent methods for communicating model results.
Mr. Ashvin Ahuja, Kevin Wiseman, and Mr. Murtaza H Syed
Assessing country risk is a core component of surveillance at the IMF. It is conducted through a comprehensive architecture, covering both bilateral and multilateral dimensions. This note describes some of the approaches used internally by Fund staff to examine a wide array of systemic risks across advanced, emerging, and low-income economies. It provides a high-level view of the theory and methodologies employed, with an on-line companion guide providing more technical details of implementation. The guide will be updated as Fund staff’s methodologies for assessing country risk continue to evolve with experience and feedback. While the results of these approaches are not published by the IMF for market sensitivity reasons, they inform risk assessments featured in bilateral surveillance as well as in the IMF’s flagship publications on global surveillance.
Mr. Suman S Basu, Mr. Marcos d Chamon, and Mr. Christopher W. Crowe
This paper summarizes a suite of early warning models to assess the probabilities of growth, fiscal, and financial crises in advanced economies and emerging markets. We estimate separate signal-extraction models for each type of crisis and sample of countries, and we use our results to generate “histories of vulnerabilities” for countries, regions, and the world. For the global financial crisis, our models report that vulnerabilities in advanced economies were rooted in the bursting of leveraged bubbles, while vulnerabilities in emerging markets stemmed from lengthy booms in credit and asset prices combined with growing weaknesses in the corporate and external sectors.
Mr. Suman S Basu, Mr. Marcos d Chamon, and Mr. Christopher W. Crowe

for risk-sharing (e.g., the reliance on debt versus equity), private-sector cushioning (e.g., corporate liquidity buffers), and policy measures (e.g., the space for fiscal and monetary stimulus). Estimation method While several early warning models have used regression analysis (either linear or limited-dependent-variable estimations), we choose instead to use a modified version of the non-parametric signal-extraction approach pioneered by Kaminsky et al. (1998) . These authors calculate a threshold value for each variable, such that observations on one