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An MVAR framework to capture extreme events in macro-prudential stress tests

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  • Guarda, Paolo
  • Rouabah, Abdelaziz
  • Theal, John
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    Abstract

    Severe financial turbulences are driven by high impact and low probability events that are the characteristic hallmarks of systemic financial stress. These unlikely adverse events arise from the extreme tail of a probability distribution and are therefore very poorly captured by traditional econometric models that rely on the assumption of normality. In order to address the problem of extreme tail events, we adopt a mixture vector autoregressive (MVAR) model framework that allows for a multi-modal distribution of the residuals. A comparison between the respective results of a VAR and MVAR approach suggests that the mixture of distributions allows for a better assessment of the effect that adverse shocks have on counterparty credit risk, the real economy and banks’ capital requirements. Consequently, we argue that the MVAR provides a more accurate assessment of risk since it captures the fat tail events often observed in time series of default probabilities. JEL Classification: C15, E44, G01, G21

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    Bibliographic Info

    Paper provided by European Central Bank in its series Working Paper Series with number 1464.

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    Date of creation: Aug 2012
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    Handle: RePEc:ecb:ecbwps:20121464

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    Related research

    Keywords: Counterparty risk; Luxembourg banking sector; MVAR; stress testing; tier 1 capital ratio;

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    1. Marcucci, Juri & Quagliariello, Mario, 2008. "Is bank portfolio riskiness procyclical: Evidence from Italy using a vector autoregression," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 18(1), pages 46-63, February.
    2. Jacobson, Tor & Linde, Jesper & Roszbach, Kasper, 2005. "Exploring interactions between real activity and the financial stance," Journal of Financial Stability, Elsevier, vol. 1(3), pages 308-341, April.
    3. Miroslav Misina & David Tessier, 2008. "Non-Linearities, Model Uncertainty, and Macro Stress Testing," Working Papers 08-30, Bank of Canada.
    4. Katarzyna Maciejowska, 2010. "Estimation Methods Comparison of SVAR Models with a Mixture of Two Normal Distributions," Central European Journal of Economic Modelling and Econometrics, CEJEME, vol. 2(4), pages 279-314, September.
    5. Dovern, Jonas & Meier, Carsten-Patrick & Vilsmeier, Johannes, 2010. "How resilient is the German banking system to macroeconomic shocks?," Journal of Banking & Finance, Elsevier, vol. 34(8), pages 1839-1848, August.
    6. 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.
    7. Rodrigo Alfaro & Mathias Drehmann, 2009. "Macro stress tests and crises: what can we learn?," BIS Quarterly Review, Bank for International Settlements, December.
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    Cited by:
    1. Alfred Wong & Tom Fong, 2013. "Gauging the Safehavenness of Currencies," Working Papers 132013, Hong Kong Institute for Monetary Research.

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