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Bayesian Analysis of the Conditional Correlation Between Stock Index Returns with Multivariate SV Models

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  • Anna Pajor

Abstract

In the paper we compare the modelling ability of discrete-time multivariate Stochastic Volatility models to describe the conditional correlations between stock index returns. We consider four trivariate SV models, which differ in the structure of the conditional covariance matrix. Specifications with zero, constant and time-varying conditional correlations are taken into account. As an example we study trivariate volatility models for the daily log returns on the WIG, SP500, and FTSE100 indexes. In order to formally compare the relative explanatory power of SV specifications we use the Bayesian principles of comparing statistic models. Our results are based on the Bayes factors and implemented through Markov Chain Monte Carlo techniques. The results indicate that the most adequate specifications are those that allow for time-varying conditional correlations and that have as many latent processes as there are conditional variances and covariances. The empirical results clearly show that the data strongly reject the assumption of constant conditional correlations.

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  • Anna Pajor, 2006. "Bayesian Analysis of the Conditional Correlation Between Stock Index Returns with Multivariate SV Models," Papers physics/0607176, arXiv.org.
    • Handle: RePEc:arx:papers:physics/0607176
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    References listed on IDEAS

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    1. Andrew Ang & Geert Bekaert, 2002. "International Asset Allocation With Regime Shifts," The Review of Financial Studies, Society for Financial Studies, vol. 15(4), pages 1137-1187.
    2. François Longin & Bruno Solnik, 2001. "Extreme Correlation of International Equity Markets," Journal of Finance, American Finance Association, vol. 56(2), pages 649-676, April.
    3. Jacek Osiewalski & Anna Pajor & Mateusz Pipień, 2006. "Bayes Factors for Bivariate GARCH and SV Models," FindEcon Chapters: Forecasting Financial Markets and Economic Decision-Making, in: Władysław Milo & Piotr Wdowiński (ed.), FindEcon Monograph Series: Advances in Financial Market Analysis, edition 1, volume 2, chapter 1, pages 15-35, University of Lodz.
    4. Anna Pajor, 2005. "Bayesian Analysis of Stochastic Volatility Model and Portfolio Allocation," FindEcon Chapters: Forecasting Financial Markets and Economic Decision-Making, in: Władysław Milo & Piotr Wdowiński (ed.), Acta Universitatis Lodziensis. Folia Oeconomica nr 192/2005 - Issues in Modeling, Forecasting and Decision-Making in Financial Markets, edition 1, volume 127, chapter 14, pages 229-249, University of Lodz.
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    Cited by:

    1. Roberto Leon-Gonzalez & Blessings Majoni, 2024. "Approximate Factor Models with a Common Multiplicative Factor for Stochastic Volatility," Working Paper series 24-04, Rimini Centre for Economic Analysis.
    2. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2016. "Common Drifting Volatility in Large Bayesian VARs," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(3), pages 375-390, July.

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