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Large dynamic covariance matrices: enhancements based on intraday data

Author

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  • Gianluca De Nard
  • Robert F. Engle
  • Olivier Ledoit
  • Michael Wolf

Abstract

Multivariate GARCH models do not perform well in large dimensions due to the so-called curse of dimensionality. The recent DCC-NL model of Engle et al. (2019) is able to overcome this curse via nonlinear shrinkage estimation of the unconditional correlation matrix. In this paper, we show how performance can be increased further by using open/high/low/close (OHLC) price data instead of simply using daily returns. A key innovation, for the improved modeling of not only dynamic variances but also of dynamic correlations, is the concept of a regularized return, obtained from a volatility proxy in conjunction with a smoothed sign of the observed return.

Suggested Citation

  • Gianluca De Nard & Robert F. Engle & Olivier Ledoit & Michael Wolf, 2020. "Large dynamic covariance matrices: enhancements based on intraday data," ECON - Working Papers 356, Department of Economics - University of Zurich, revised Jan 2022.
  • Handle: RePEc:zur:econwp:356
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    Citations

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    Cited by:

    1. Gianluca De Nard & Robert F. Engle & Bryan Kelly, 2023. "Factor mimicking portfolios for climate risk," ECON - Working Papers 429, Department of Economics - University of Zurich.
    2. Fiszeder, Piotr & Fałdziński, Marcin & Molnár, Peter, 2023. "Attention to oil prices and its impact on the oil, gold and stock markets and their covariance," Energy Economics, Elsevier, vol. 120(C).
    3. Rafael Alves & Diego S. de Brito & Marcelo C. Medeiros & Ruy M. Ribeiro, 2023. "Forecasting Large Realized Covariance Matrices: The Benefits of Factor Models and Shrinkage," Papers 2303.16151, arXiv.org.
    4. Bucci, Andrea & Palomba, Giulio & Rossi, Eduardo, 2023. "The role of uncertainty in forecasting volatility comovements across stock markets," Economic Modelling, Elsevier, vol. 125(C).
    5. Bongiorno, Christian & Challet, Damien, 2023. "Non-linear shrinkage of the price return covariance matrix is far from optimal for portfolio optimization," Finance Research Letters, Elsevier, vol. 52(C).
    6. Anatolyev, Stanislav & Pyrlik, Vladimir, 2022. "Copula shrinkage and portfolio allocation in ultra-high dimensions," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    7. Wenyang Huang & Huiwen Wang & Shanshan Wang, 2021. "Dimension reduction of open-high-low-close data in candlestick chart based on pseudo-PCA," Papers 2103.16908, arXiv.org.
    8. De Nard, Gianluca & Zhao, Zhao, 2023. "Using, taming or avoiding the factor zoo? A double-shrinkage estimator for covariance matrices," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 23-35.
    9. Fiszeder, Piotr & Fałdziński, Marcin & Molnár, Peter, 2023. "Modeling and forecasting dynamic conditional correlations with opening, high, low, and closing prices," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 308-321.

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    More about this item

    Keywords

    Dynamic conditional correlations; intraday data; Markowitz portfolio selection; multivariate GARCH; nonlinear shrinkage;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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