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Formulating hypothetical scenarios in correlation stress testing via a Bayesian framework

Author

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  • Yu, Philip L.H.
  • Li, W.K.
  • Ng, F.C.

Abstract

Correlation stress testing refers to the correlation matrix adjustment to evaluate potential impact of the changes in correlations under financial crises. There are two categories, sensitivity tests and scenario tests. For a scenario test, the correlation matrix is adjusted to mimic the situation under an underlying stress event. It is only natural that when some correlations are altered, the other correlations (peripheral correlations) should vary as well. However, most existing methods ignore this potential change in peripheral correlations. In this paper, we propose a Bayesian correlation adjustment method to give a new correlation matrix for a scenario test based on the original correlation matrix and views on correlations such that peripheral correlations are altered according to the dependence structure of empirical correlations. The algorithm of posterior simulation is also extended so that two correlations can be updated in one Gibbs sampler step. This greatly enhances the rate of convergence. The proposed method is applied to an international stock portfolio dataset.

Suggested Citation

  • Yu, Philip L.H. & Li, W.K. & Ng, F.C., 2014. "Formulating hypothetical scenarios in correlation stress testing via a Bayesian framework," The North American Journal of Economics and Finance, Elsevier, vol. 27(C), pages 17-33.
    • Handle: RePEc:eee:ecofin:v:27:y:2014:i:c:p:17-33
    DOI: 10.1016/j.najef.2013.10.002
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    References listed on IDEAS

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    1. So, Mike K.P. & Wong, Jerry & Asai, Manabu, 2013. "Stress testing correlation matrices for risk management," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 310-322.
    2. Vineer Bhansali & Mark B. Wise, 2001. "Forecasting Portfolio Risk in Normal and Stressed Markets," Papers nlin/0108022, arXiv.org, revised Sep 2001.
    3. Frederick Wong, 2003. "Efficient estimation of covariance selection models," Biometrika, Biometrika Trust, vol. 90(4), pages 809-830, December.
    4. John C. Liechty, 2004. "Bayesian correlation estimation," Biometrika, Biometrika Trust, vol. 91(1), pages 1-14, March.
    5. A. Leon & J. E. Peris & J. Silva & B. Subiza, 2002. "A note on adjusting correlation matrices," Applied Mathematical Finance, Taylor & Francis Journals, vol. 9(1), pages 61-67.
    6. François Longin & Bruno Solnik, 2001. "Extreme Correlation of International Equity Markets," Journal of Finance, American Finance Association, vol. 56(2), pages 649-676, April.
    7. Umberto Cherubini & Giovanni Lunga, 1999. "Stress testing techniques and value-at-risk measures: A unified approach," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 22(1), pages 77-99, March.
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