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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.
Despite increased need for top-down stress tests of financial institutions, performing them is challenging owing to the absence of granular information on banks’ trading and loan portfolios. To deal with these data shortcomings, this paper presents a market-based structural top-down stress testing methodology that relies in market-based measures of a bank's probability of default and structural models of default risk to infer the capital losses they could experience in stress scenarios. As an illustration, the methodology is applied to a set of banks in an advanced emerging market economy.
Credibility is the bedrock of any crisis stress test. The use of stress tests to manage systemic risk was introduced by the U.S. authorities in 2009 in the form of the Supervisory Capital Assessment Program. Since then, supervisory authorities in other jurisdictions have also conducted similar exercises. In some of those cases, the design and implementation of certainelements of the framework have been criticized for their lack of credibility. This paper proposes a set of guidelines for constructing an effective crisis stress test. It combines financial markets impact studies of previous exercises with relevant case study information gleaned from those experiences to identify the key elements and to formulate their appropriate design. Pertinent concepts, issues and nuances particular to crisis stress testing are also discussed. The findings may be useful for country authorities seeking to include stress tests in their crisis management arsenal, as well as for the design of crisis programs.
Over the last decade, stress testing has become a central aspect of the Fund’s bilateral and multilateral surveillance work. Recently, more emphasis has also been placed on the role of insurance for financial stability analysis. This paper reviews the current state of system-wide solvency stress tests for insurance based on a comparative review of national practices and the experiences from Fund’s FSAP program with the aim of providing practical guidelines for the coherent and consistent implementation of such exercises. The paper also offers recommendations on improving the current insurance stress testing approaches and presentation of results.
Stress tests are used in risk management by banks in order to determine how certain crisis scenarios would affect the value of their portfolios, and by public authorities for financial stability purposes. Until the first half of 2007, interest in stress-testing was largely restricted to practitioners. Since then, the global financial system has been hit by deep turbulences, including the fallout from sub-prime mortgage lending. Many observers have pointed out that the severity of the crisis has been largely due to its unexpected nature and have claimed that a more extensive use of stress-testing methodologies would have helped to alleviate the repercussions of the crisis. This book analyses the theoretical underpinnings, as well as the practical aspects, of applying such methodologies. Building on the experience gained by the economists of many national and international financial authorities, it provides an updated toolkit for both practitioners and academics.
Stress testing is a useful and increasingly popular, yet sometimes misunderstood, method of analyzing the resilience of financial systems to adverse events. This paper aims to help demystify stress tests, and illustrate their strengths and weaknesses. Using an Excel-based exercise with institution-by-institution data, readers are walked through stress testing for credit risk, interest rate and exchange rate risks, liquidity risk and contagion risk, and are guided in the design of stress testing scenarios. The paper also describes the links between stress testing and other analytical tools, such as financial soundness indicators and supervisory early warning systems. Furthermore, it includes surveys of stress testing practices in central banks and the IMF.
Reverse stress testing was introduced in risk management as a regulatory tool for financial institutions more than a decade ago. The recent Covid-19 crisis illustrates its relevance and highlights the need for a systematic re-thinking of tail risks in the banking sector. This book addresses the need for practical guidance describing the entire reverse stress testing process. Reverse Stress Testing in Banking features contributions from a diverse range of established practitioners and academics. Organized in six parts, the book presents a series of contributions providing an in-depth understanding of: Regulatory requirements and ways to address them Quantitative and qualitative approaches to apply reverse stress testing at different levels – from investment portfolios and individual banks to the entire banking system The use of artificial intelligence, machine learning and quantum computing to gain insights into and address banks’ structural weaknesses Opportunities to co-integrate reverse stress testing with recovery and resolution planning Governance and processes for board members and C-suite executives Readers will benefit from the case studies, use cases from practitioners, discussion questions, recommendations and innovative practices provided in this insightful and pioneering book.
The objective of this paper is to present an integrated tool suite for IFRS 9- and CECL-compatible estimation in top-down solvency stress tests. The tool suite serves as an illustration for institutions wishing to include accounting-based approaches for credit risk modeling in top-down stress tests.
Despite increased need for top-down stress tests of financial institutions, performing them is challenging owing to the absence of granular information on banks’ trading and loan portfolios. To deal with these data shortcomings, this paper presents a market-based structural top-down stress testing methodology that relies in market-based measures of a bank's probability of default and structural models of default risk to infer the capital losses they could experience in stress scenarios. As an illustration, the methodology is applied to a set of banks in an advanced emerging market economy.
This paper presents a "second-generation" solvency stress testing framework extending applied stress testing work centered on Cihák (2007). The framework seeks enriching stress tests in terms of risk-sensitivity, while keeping them flexible, transparent, and user-friendly. The main contributions include (a) increasing the risk-sensitivity of stress testing by capturing changes in risk-weighted assets (RWAs) under stress, including for non-internal ratings based (IRB) banks (through a quasi-IRB approach); (b) providing stress testers with a comprehensive platform to use satellite models, and to define various assumptions and scenarios; (c) allowing stress testers to run multi-year scenarios (up to five years) for hundreds of banks, depending on the availability of data. The framework uses balance sheet data and is Excel-based with detailed guidance and documentation.