An MVAR Framework to Capture Extreme Events in Macroprudential Stress Tests
AbstractThe stress testing literature abounds with reduced-form macroeconomic models that are used to forecast the evolution of the macroeconomic environment in the context of a stress testing exercise. These models permit supervisors to estimate counterparty risk under both baseline and adverse scenarios. However, the large majority of these models are founded on the assumption of normality of the innovation series. While this assumption renders the model tractable, it fails to capture the observed frequency of distant tail events that represent the hallmark of systemic financial stress. Consequently, these kinds of macro models tend to underestimate the actual level of credit risk. This also leads to an inaccurate assessment of the degree of systemic risk inherent in the financial sector. Clearly this may have significant implications for macro-prudential policy makers. One possible way to overcome such a limitation is to introduce a mixture of distributions model in order to better capture the potential for extreme events. Based on the methodology developed by Fong, Li, Yau and Wong (2007), we have incorporated a macroeconomic model based on a mixture vector autoregression (MVAR) into the stress testing framework of Rouabah and Theal (2010) that is used at the Banque centrale du Luxembourg. This allows the counterparty credit risk model to better capture extreme tail events in comparison to models based on assuming normality of the distributions underlying the macro models. We believe this approach facilitates a more accurate assessment of credit risk.
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Bibliographic InfoPaper provided by Central Bank of Luxembourg in its series BCL working papers with number 63.
Length: 37 pages
Date of creation: Oct 2011
Date of revision:
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Web page: http://www.bcl.lu/
financial stability; stress testing; MVAR; mixture of normals; VAR; tier 1 capital ratio; counterparty risk; Luxembourg banking sector;
Find related papers by JEL classification:
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
- G01 - Financial Economics - - General - - - Financial Crises
- G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-11-01 (All new papers)
- NEP-BAN-2011-11-01 (Banking)
- NEP-CBA-2011-11-01 (Central Banking)
- NEP-ECM-2011-11-01 (Econometrics)
- NEP-RMG-2011-11-01 (Risk Management)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Glenn Hoggarth & Steffen Sorensen & Lea Zicchino, 2005. "Stress tests of UK banks using a VAR approach," Bank of England working papers 282, Bank of England.
- Abdelaziz Rouabah & John Theal, 2010. "Stress testing: The impact of shocks on the capital needs of the Luxembourg banking sector," BCL working papers 47, Central Bank of Luxembourg.
- Katarzyna Maciejowska, 2010. "Estimation methods comparison of SVAR model with the mixture of two normal distributions - Monte Carlo analysis," Economics Working Papers ECO2010/27, European University Institute.
- Romuald Morhs, 2010. "Monetary Policy Transmission and Macroeconomic Dynamics in Luxembourg: Results from a VAR Analysis," BCL working papers 49, Central Bank of Luxembourg.
- Yan, Yan & Barry, Peter J. & Paulson, Nicholas D. & Schnitkey, Gary D., 2009. "Measurement of Farm Credit Risk: SUR Model and Simulation Approach," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49222, Agricultural and Applied Economics Association.
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