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Dynamic Modeling Of High-Dimensional Correlation Matrices In Finance

Author

Listed:
  • VASYL GOLOSNOY

    (Institute of Statistics and Econometrics, CAU Kiel, Olshausenstr. 40, Kiel 24118, Germany)

  • HELMUT HERWARTZ

    (Institute of Statistics and Econometrics, CAU Kiel, Olshausenstr. 40, Kiel 24118, Germany)

Abstract

A class of dynamic factor and dynamic panel models is proposed for daily high dimensional correlation matrices of asset returns. These flexible semiparametric predictors process ultra high frequency information and allow to exploit both realized correlation matrices and exogenous factors for forecasting purposes. The Fisher-z transformation offers the transmission from (factor and panel) time series models operating on unrestricted random variables to bounded correlation forecasts. Our methodology is contrasted with prominent alternative correlation models. Based on economic performance criteria dynamic factor models turn out to carry the highest predictive content.

Suggested Citation

  • Vasyl Golosnoy & Helmut Herwartz, 2012. "Dynamic Modeling Of High-Dimensional Correlation Matrices In Finance," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 15(05), pages 1-22.
    • Handle: RePEc:wsi:ijtafx:v:15:y:2012:i:05:n:s0219024912500355
    DOI: 10.1142/S0219024912500355
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    References listed on IDEAS

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    1. Herwartz, Helmut & Golosnoy, Vasyl, 2007. "Semiparametric Approaches to the Prediction of Conditional Correlation Matrices in Finance," Economics Working Papers 2007-23, Christian-Albrechts-University of Kiel, Department of Economics.
    2. Cavit Pakel & Neil Shephard & Kevin Sheppard & Robert F. Engle, 2021. "Fitting Vast Dimensional Time-Varying Covariance Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(3), pages 652-668, July.
    3. Gençay, Ramazan & Dacorogna, Michel & Muller, Ulrich A. & Pictet, Olivier & Olsen, Richard, 2001. "An Introduction to High-Frequency Finance," Elsevier Monographs, Elsevier, edition 1, number 9780122796715.
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    Cited by:

    1. Roland Weigand, 2014. "Matrix Box-Cox Models for Multivariate Realized Volatility," Working Papers 144, Bavarian Graduate Program in Economics (BGPE).
    2. Vasyl Golosnoy, 2018. "Sequential monitoring of portfolio betas," Statistical Papers, Springer, vol. 59(2), pages 663-684, June.

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