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In the presence of adverse macroeconomic shocks, simultaneous capital losses in multiple banks can prompt them to contract their balance sheets. These bank responses generate externalities that propagate in the form of macro-financial feedback loops. This paper develops a credit response and externalities analysis model (CREAM) that integrates a disaggregated banking sector into an otherwise standard macroeconomic structural vector autoregressive model. It shows that accounting for macro-financial feedback loops can significantly affect macroeconomic outcomes and bank-specific stress tests results. The heterogeneity in bank lending responses matters: it determines how each bank fares under adverse conditions and the external effects that banks impose on each other and on economic activity. The model can thus be used to assess the contributions of individual banks to systemic risk along the time dimension.
Discover current uses and future development of stress tests, the most innovative regulatory tool to prevent and fight financial crises.
This paper explains specifics of stress testing at the IMF. After a brief section on the evolution of stress tests at the IMF, the paper presents the key steps of an IMF staff stress test. They are followed by a discussion on how IMF staff uses stress tests results for policy advice. The paper concludes by identifying remaining challenges to make stress tests more useful for the monitoring of financial stability and an overview of IMF staff work program in that direction. Stress tests help assess the resilience of financial systems in IMF member countries and underpin policy advice to preserve or restore financial stability. This assessment and advice are mainly provided through the Financial Sector Assessment Program (FSAP). IMF staff also provide technical assistance in stress testing to many its member countries. An IMF macroprudential stress test is a methodology to assess financial vulnerabilities that can trigger systemic risk and the need of systemwide mitigating measures. The definition of systemic risk as used by the IMF is relevant to understanding the role of its stress tests as tools for financial surveillance and the IMF’s current work program. IMF stress tests primarily apply to depository intermediaries, and, systemically important banks.
During the COVID-19 pandemic and global financial crisis, governments swiftly served as financiers of last resort through large financial support measures (FSMs) such as loan and guarantee programs and equity injections in firms. This Staff Discussion Note argues that such FSMs prevented bankruptcies and attenuated the recession by increasing firms’ liquidity, reducing risk premiums, and boosting confidence. But FSMs also carry large and long-lasting fiscal costs and risks. The note presents recommendations for managing the legacies of the COVID-19 programs and preparing for future crises. Ideally, FSMs should be assessed and included in budget plans, though a balance needs to be struck between speed and scrutiny.
This paper reviews quantitative tools of financial stability assessments under the Financial Sector Assessment Program (FSAP). A key focus of FSAPs is on methodologies to gauge risks on a system-wide level and propose mitigating measures. Therefore, the paper concentrates on the main elements of the FSAP’s macroprudential stress testing framework:(i) the interaction among solvency, liquidity, and contagion risks in the banking sector, (ii) the assessment of the health of nonbank financial institutions (NBFIs), their interactions with banks and their impact on financial markets, (iii) the assessment of the health of nonfinancial sectors and their links to the financial sector, and (iv) macroprudential policy analysis. The paper also reviews recent improvements in microprudential bank solvency stress testing—an important foundation for the macroprudential stress testing framework—and discusses new tools for emerging risks (climate change, fintech, and cyber).
This paper studies the transmission of bank capital shocks to loan supply in Indonesia. A series of theoretically founded dynamic panel data models are estimated and find nonlinear effects of capital on loan growth: the response of weaker banks to changes in their capital positions is larger than that of stronger banks. This non-linearity implies that not only the level of capital but also its distribution across banks in the financial system affects the transmission of shocks to aggregate lending. Likewise, the effects of bank recapitalization on loan growth depend on banks’ starting capital positions and the size of capital injections.
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.
Rather than taking on more risk, US insurers hit hard by the crisis pulled back from risk taking, relative to insurers not hit as hard by the crisis. Capital requirements alone do not explain this risk reduction: insurers hit hard reduced risk within assets with identical regulatory treatment. State level US insurance regulation makes it unlikely this risk reduction was driven by moral suasion. Other financial institutions also reduce risk after large shocks: the same approach applied to banks yields similar results. My results suggest that, at least in some circumstances, franchise value can dominate, making gambling for resurrection too risky.
Model selection and forecasting in stress tests can be facilitated using machine learning techniques. These techniques have proved robust in other fields for dealing with the curse of dimensionality, a situation often encountered in applied stress testing. Lasso regressions, in particular, are well suited for building forecasting models when the number of potential covariates is large, and the number of observations is small or roughly equal to the number of covariates. This paper presents a conceptual overview of lasso regressions, explains how they fit in applied stress tests, describes its advantages over other model selection methods, and illustrates their application by constructing forecasting models of sectoral probabilities of default in an advanced emerging market economy.
A former Michigan congressman and member of the Reagan administration describes how interference in the financial markets has contributed to the national debt and has damaging and lasting repercussions.