Formulating hypothetical scenarios in correlation stress testing via a Bayesian framework

Philip L.H. YU, Wai Keung LI, F.C. NG

Research output: Contribution to journalArticle

1 Citation (Scopus)

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. Copyright © 2013 Elsevier Inc. All rights reserved.
Original languageEnglish
Pages (from-to)17-33
JournalNorth American Journal of Economics and Finance
Volume27
Early online dateNov 2013
DOIs
Publication statusPublished - Jan 2014

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Scenarios
Stress testing
Correlation matrix
Gibbs sampler
Simulation
Rate of convergence
Financial crisis
Dependence structure

Citation

Yu, P. 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, 27, 17-33. doi: 10.1016/j.najef.2013.10.002

Keywords

  • Correlation stress testing
  • Scenario test
  • Bayesian estimation
  • Block Gibbs sampling