This paper contributes to the sparse literature on reverse stress testing, the necessity of which is emphasized by banks' supervisory authorities. While, for regular stress tests, scenarios are chosen based on historical experience or expert knowledge and their influence on the bank's survivability is tested, reverse stress tests aim to find exactly those scenarios that cause the bank to cross the frontier between survival and default. Afterward, the most likely of these scenarios has to be found. We argue that bottom-up approaches, as specific integrated risk management techniques, are ideal candidates for carrying out quantitative reverse stress tests because they model interactions between different risk types already on the level of the individual financial instruments and risk factors. This is exemplified with an extended CreditMetrics model that exhibits correlated interest rates and rating-specific credit-spread risk.
This paper studies the effect on economic capital from integrating interest rate and credit spread risk into credit portfolio models. By using fixed forward rates, most credit portfolio models currently employed in the banking industry ignore these risk factors. In contrast to previous studies, this paper accounts for correlated transition risk, credit spread risk, interest rate risk and also recovery rate risk. The simulations show that the error made when neglecting the stochastic nature of interest rates or credit spreads is significant, especially for high quality credit portfolios with low correlations between the obligors' asset returns.
Reproduction permitted only if source is stated.ISBN 978-3-95729-185-1 (Printversion) Non-technical summary Research QuestionIn response to the financial crisis [2007][2008][2009], regulatory authorities have strengthened the importance of stress test methodologies and particularly emphasized the role of reverse stress tests. Reverse stress tests look exactly for those scenarios which lead to a very unfavorable event for a bank, for example, an equity-exhausting loss, a non-fulfillment of the capital adequacy requirements or illiquidity. More generally, scenarios shall be identified that lead to an outcome in which the bank's business plan becomes unviable and the bank insolvent. In this paper, we show how a fully-fledged macroeconomic reverse stress test for credit and interest rate risk can be implemented which is in line with the new regulatory requirements. ContributionThis paper contributes to the sparse quantitative reverse stress test literature and sketches a framework which allows to model interactions between different risk factors at the level of individual financial instruments and risk factors. The focus lies on the presentation of the calibration procedure and on a detailed discussion of practical implementation issues. ResultsWe search for the most likely scenario which exhausts the bank's equity. It turns out that this so-called reverse stress test scenario, which is given by a combination of macroeconomic variables, is economically reasonable for the assumed bank portfolio. In particular, for a bank which engages in maturity-transformation, the most likely reverse stress test scenario implies a steeper interest rate curve and an economic downturn. However, the paper also reveals that due to high data requirements and intensive computational efforts, reverse stress tests are exposed to considerable model and estimation risk which makes numerous robustness checks necessary. AbstractReverse stress tests are a relatively new stress test instrument that aims at finding exactly those scenarios that cause a bank to cross the frontier between survival and default. Afterward, the scenario which is most probable has to be identified. This paper sketches a framework for a quantitative reverse stress test for maturity-transforming banks that are exposed to credit and interest rate risk and demonstrates how the model can be calibrated empirically. The main features of the proposed framework are: 1) The necessary steps of a reverse stress test (solving an inversion problem and computing the scenario probabilities) can be performed within one model, 2) Scenarios are characterized by realizations of macroeconomic risk factors, 3) Principal component analysis helps to reduce the dimensionality of the space of systematic risk factors, 4) Due to data limitations, the results of reverse stress tests are exposed to considerable model and estimation risk, which makes numerous robustness checks necessary.Keywords: copula functions, extreme value theory, principal component analysis, reverse stress testing JEL classi...
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