This paper presents a "second-generation" solvency stress testing framework extending applied stress testing work centered on Čihák (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. (please click on the link "Link to data for this title").
The global financial crisis has placed the spotlight squarely on bank stress tests. Stress tests conducted in the lead-up to the crisis, including those by IMF staff, were not always able to identify the right risks and vulnerabilities. Since then, IMF staff has developed more robust stress testing methods and models and adopted a more coherent and consistent approach. This paper articulates the solvency stress testing framework that is being applied in the IMF's surveillance of member countries' banking systems, and discusses examples of its actual implementation in FSAPs to 18 countries which are in the group comprising the 25 most systemically important financial systems ("S-25") plus other G-20 countries. In doing so, the paper also offers useful guidance for readers seeking to develop their own stress testing frameworks and country authorities preparing for FSAPs. A detailed Stress Test Matrix (STeM) comparing the stress test parameters applie in each of these major country FSAPs is provided, together with our stress test output templates.
In credit risk modelling, the correlation of unobservable asset returns is a crucial component for the measurement of portfolio risk. In this paper, we estimate asset correlations from monthly time series of Moody's KMV asset values for around 2,000 European firms from 1996 to 2004. We compare correlation and value-atrisk (VaR) estimates in a one-factor or market model and a multi-factor or sector model. Our main finding is a complex interaction of credit risk correlations and default probabilities affecting total credit portfolio risk. Differentiation between industry sectors when using the sector model instead of the market model has only a secondary effect on credit portfolio risk, at least for the underlying credit portfolio. Averaging firm-dependent asset correlations on a sector level can, however, cause a substantial underestimation of the VaR in a portfolio with heterogeneous borrower size. This result holds for the market as well as the sector model. Furthermore, the VaR of the IRB model is more stable over time than the VaR of the market model and the sector model, while its distance from the other two models fluctuates over time. Keywords: Asset correlations, sector concentration, credit portfolio risk JEL Classification: G 21, C 15 Non-Technical SummaryThe correlations between two firms' asset-value returns, commonly referred to as asset correlation, are a key factor in measuring the credit risk of loan portfolios.Since asset values are not directly observable, we employ time series of asset values of European firms which are based on the Moody's KMV model. A descriptive analysis of these asset correlations and correlations with industry sector indices is a first contribution of this paper. We observe a considerable fluctuation of asset correlations which suggests further research on their stability over time. The second contribution is a comprehensive analysis how asset correlations as input parameters into a credit risk model affect the value-at-risk which is a measure of credit risk for a portfolio. We observe that borrower-dependent asset correlations produces a substantially higher value-at-risk than median asset correlations computed on a sector level. We attribute this finding mainly to the empirical fact that asset correlations tend to increase with borrower size, which means that sector averages understate the correlation effect. We conclude that the way asset correlations are used in the credit risk model also has a substantial impact on the risk assessment of a portfolio. This methodological challenge adds to the empirical challenge of estimating asset correlations reliably. Furthermore, our results suggest that the regulatory capital charge of the internal ratings-based approach of Basel II is less volatile over time than value-at-risk in the other credit risk models in our study. Nichttechnische Zusammenfassung
This Working Paper should not be reported as representing the views of the IMF.The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those of the IMF/BIS/BCBS or IMF/BIS/BCBS policy. Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate.Rules of thumb can be useful in undertaking quick, robust, and readily interpretable bank stress tests. Such rules of thumb are proposed for the behavior of banks' capital ratios and key drivers thereof-primarily credit losses, income, credit growth, and risk weights-in advanced and emerging economies, under more or less severe stress conditions. The proposed rules imply disproportionate responses to large shocks, and can be used to quantify the cyclical behaviour of capital ratios under various regulatory approaches.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.