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Dynamic Asset Correlations Based on Vines

Author

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  • Benjamin Poignard

    (ENSAE, CREST and University Paris-Dauphine)

  • Jean-Davis Fermanian

    (ENSAE and CREST)

Abstract

We develop a new method for generating dynamics of conditional correlation matrices between asset returns. These correlation matrices will be parameterized by a subset of their partial correlations, whose structure will be described by an undirected graph called \vine". Since such partial correlation processes can be speci ed separately, our approach provides very exible and potentially parsimonious multivariate processes. We introduce the so-called\vine-GARCH" class of processes and describe a quasi-maximum likelihood (QML) estimation procedure. Compared to other usual techniques, particularly for the Dynamic Conditional Correlation family, inference is simpler and can be led equation per equation. We compare our models with some DCC-type speci cations through some simulated experiments and we evaluate their empirical performances by exploiting a database of daily stock returns.

Suggested Citation

  • Benjamin Poignard & Jean-Davis Fermanian, 2014. "Dynamic Asset Correlations Based on Vines," Working Papers 2014-46, Center for Research in Economics and Statistics.
    • Handle: RePEc:crs:wpaper:2014-46
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    1. Jean-David Fermanian & Benjamin Poignard & Panos Xidonas, 2025. "Model-based vs. agnostic methods for the prediction of time-varying covariance matrices," Annals of Operations Research, Springer, vol. 346(1), pages 511-548, March.
    2. Shanshan Jiang & Jie Wang & Ruiting Dong & Yutong Li & Min Xia, 2023. "Systemic Risk with Multi-Channel Risk Contagion in the Interbank Market," Sustainability, MDPI, vol. 15(3), pages 1-24, February.
    3. Gilles Boevi Koumou, 2020. "Diversification and portfolio theory: a review," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 34(3), pages 267-312, September.

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