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The economic value of flexible dynamic correlation models

Author

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  • Dimitrios P. Louzis

    (Bank of Greece)

Abstract

This article assesses the ability of flexible dynamic correlation specifications to improve asset allocation decisions. To that end, we use the recently proposed Rotated Dynamic Conditional Correlation (RDCC) model that enables the estimation of models with high degree of parameterization and large number of assets. We also extend the RDCC model to incorporate 'rotated' realized correlation measures which exploit the information content of intra-day data. The empirical evidence, based on ten US equities and three years of out-of-sample forecasting (2007-2009), support the use of flexible diagonal RDCC specifications for portfolio management purposes. However, simpler scalar specifications enhanced with realized correlation measures can produce superior or in some cases similar results. Overall, our findings give evidence in favor of inter-daily flexible RDCC models for asset allocation purposes when the computation of realized correlation measures is practically unfeasible.

Suggested Citation

  • Dimitrios P. Louzis, 2015. "The economic value of flexible dynamic correlation models," Economics Bulletin, AccessEcon, vol. 35(1), pages 774-782.
    • Handle: RePEc:ebl:ecbull:eb-15-00194
    as

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    References listed on IDEAS

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    1. Sébastien Laurent & Jeroen V. K. Rombouts & Francesco Violante, 2012. "On the forecasting accuracy of multivariate GARCH models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(6), pages 934-955, September.
    2. Valeri Voev, 2009. "On the Economic Evaluation of Volatility Forecasts," CREATES Research Papers 2009-56, Department of Economics and Business Economics, Aarhus University.
    3. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
    4. Noureldin, Diaa & Shephard, Neil & Sheppard, Kevin, 2014. "Multivariate rotated ARCH models," Journal of Econometrics, Elsevier, vol. 179(1), pages 16-30.
    5. repec:taf:jnlbes:v:30:y:2012:i:2:p:212-228 is not listed on IDEAS
    6. Roxana Chiriac & Valeri Voev, 2011. "Modelling and forecasting multivariate realized volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(6), pages 922-947, September.
    7. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    8. Varneskov, Rasmus & Voev, Valeri, 2013. "The role of realized ex-post covariance measures and dynamic model choice on the quality of covariance forecasts," Journal of Empirical Finance, Elsevier, vol. 20(C), pages 83-95.
    9. Noureldin, Diaa & Shephard, Neil & Sheppard, Kevin, 2014. "Multivariate rotated ARCH models," Scholarly Articles 34650305, Harvard University Department of Economics.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Dynamic Correlations; Rich parameterization; Realized correlation; Portfolio selection.;
    All these keywords.

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • G0 - Financial Economics - - General

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