The growth-at-risk (GaR) framework links current macrofinancial conditions to the distribution of future growth. Its main strength is its ability to assess the entire distribution of future GDP growth (in contrast to point forecasts), quantify macrofinancial risks in terms of growth, and monitor the evolution of risks to economic activity over time. By using GaR analysis, policymakers can quantify the likelihood of risk scenarios, which would serve as a basis for preemptive action. This paper offers practical guidance on how to conduct GaR analysis and draws lessons from country case studies. It also discusses an Excel-based GaR tool developed to support the IMF’s bilateral surveillance efforts.

The growth-at-risk (GaR) framework links current macrofinancial conditions to the distribution of future growth. Its main strength is its ability to assess the entire distribution of future GDP growth (in contrast to point forecasts), quantify macrofinancial risks in terms of growth, and monitor the evolution of risks to economic activity over time. By using GaR analysis, policymakers can quantify the likelihood of risk scenarios, which would serve as a basis for preemptive action. This paper offers practical guidance on how to conduct GaR analysis and draws lessons from country case studies. It also discusses an Excel-based GaR tool developed to support the IMF’s bilateral surveillance efforts.

The growth-at-risk (GaR) framework links current macrofinancial conditions to the distribution of future growth. Its main strength is its ability to assess the entire distribution of future GDP growth (in contrast to point forecasts), quantify macrofinancial risks in terms of growth, and monitor the evolution of risks to economic activity over time. By using GaR analysis, policymakers can quantify the likelihood of risk scenarios, which would serve as a basis for preemptive action. This paper offers practical guidance on how to conduct GaR analysis and draws lessons from country case studies. It also discusses an Excel-based GaR tool developed to support the IMF’s bilateral surveillance efforts.

The audited consolidated financial statements of the International Monetary Fund as of April 30, 2019 and 2018

In this paper we propose a novel approach to obtain the predictive density of global GDP growth. It hinges upon a bottom-up probabilistic model that estimates and combines single countries’ predictive GDP growth densities, taking into account cross-country interdependencies. Speci?cally, we model non-parametrically the contemporaneous interdependencies across the United States, the euro area, and China via a conditional kernel density estimation of a joint distribution. Then, we characterize the potential ampli?cation e?ects stemming from other large economies in each region—also with kernel density estimations—and the reaction of all other economies with para-metric assumptions. Importantly, each economy’s predictive density also depends on a set of observable country-speci?c factors. Finally, the use of sampling techniques allows us to aggregate individual countries’ densities into a world aggregate while preserving the non-i.i.d. nature of the global GDP growth distribution. Out-of-sample metrics con?rm the accuracy of our approach.

This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.

MCM conducted a survey in December 2010 to take stock of international experiences with financial stability and the evolving macroprudential policy framework. The survey was designed to seek information in three broad areas: the institutional setup for macroprudential policy, the analytical approach to systemic risk monitoring, and the macroprudential policy toolkit. The survey was sent to 63 countries and the European Central Bank (ECB), including all countries in the G-20 and those subject to mandatory Financial Sector Assessment Programs (FSAPs). The target list is designed to cover a broad range of jurisdictions in all regions, but more weight is given to economies that are systemically important (see Annex for details). The response rate is 80 percent. This note provides a summary of the survey’s main findings.

The September 2011 edition of the World Economic Outlook assesses the prospects for the global economy, which is now in a dangerous new phase. Global activity has weakened and become more uneven, confidence has fallen sharply recently, and downside risks are growing. Against a backdrop of unresolved structural fragilities, a barrage of shocks hit the international economy this year, including the devastating Japanese earthquake and tsunami, unrest in some oil-producing countries, and the major financial turbulence in the euro area. Two of the forces now shaping the global economy are high and rising commodity prices and the need for many economies to address large budget deficits. Chapter 3 examines the inflationary effects of commodity price movements and the appropriate monetary policy response. Chapter 4 explores the implications of efforts by advanced economies to restore fiscal sustainability and by emerging and developing economies to tighten fiscal policy to rebuild fiscal policy room and in some cases to restrain overheating pressures.

This Financial System Stability Assessment paper discusses that Canada has enjoyed favorable macroeconomic outcomes over the past decades, and its vibrant financial system continues to grow robustly. However, macrofinancial vulnerabilities—notably, elevated household debt and housing market imbalances—remain substantial, posing financial stability concerns. Various parts of the financial system are directly exposed to the housing market and/or linked through housing finance. The financial system would be able to manage severe macrofinancial shocks. Major deposit-taking institutions would remain resilient, but mortgage insurers would need additional capital in a severe adverse scenario. Housing finance is broadly resilient, notwithstanding some weaknesses in the small non-prime mortgage lending segment. Although banks’ overall capital buffers are adequate, additional required capital for mortgage exposures, along with measures to increase risk-based differentiation in mortgage pricing, would be desirable. This would help ensure adequate through-the cycle buffers, improve mortgage risk-pricing, and limit procyclical effects induced by housing market corrections.

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.