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Modeling Realized Covariance Matrices: A Class of Hadamard Exponential Models

Author

Listed:
  • Bauwens, Luc

    (Université catholique de Louvain, LIDAM/CORE, Belgium)

  • Otranto, Edoardo

    (Universita di Messina)

Abstract

Time series of realized covariance matrices can be modeled in the conditional autoregressive Wishart model family via dynamic correlations or via dynamic covariances. Extended parameterizations of these models are proposed, which imply a specific and time-varying impact parameter of the lagged realized covariance (or correlation) on the next conditional covariance (or correlation) of each asset pair. The proposed extensions guarantee the positive definiteness of the conditional covariance or correlation matrix with simple parametric restrictions, while keeping the number of parameters fixed or linear with respect to the number of assets. Two empirical studies reveal that the extended models have superior forecasting performances than their simpler versions and benchmark models.

Suggested Citation

  • Bauwens, Luc & Otranto, Edoardo, 2022. "Modeling Realized Covariance Matrices: A Class of Hadamard Exponential Models," LIDAM Reprints CORE 3202, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvrp:3202
    DOI: https://doi.org/10.1093/jjfinec/nbac007
    Note: In: Journal of Financial Econometrics, 2022
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    Cited by:

    1. Bauwens, Luc & Xu, Yongdeng, 2025. "The contribution of realized variance–covariance models to the economic value of volatility timing," International Journal of Forecasting, Elsevier, vol. 41(3), pages 1165-1183.
    2. Bauwens, Luc & Xu, Yongdeng, 2023. "The contribution of realized covariance models to the economic value of volatility timing," Cardiff Economics Working Papers E2023/20, Cardiff University, Cardiff Business School, Economics Section.
    3. Bauwens, Luc & Otranto, Edoardo, 2023. "Realized Covariance Models with Time-varying Parameters and Spillover Effects," LIDAM Discussion Papers CORE 2023019, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. Jiawen Luo & Shengjie Fu & Oguzhan Cepni & Rangan Gupta, 2025. "The Role of Uncertainty in Forecasting Realized Covariance of US State-Level Stock Returns: A Reverse-MIDAS Approach," Working Papers 202501, University of Pretoria, Department of Economics.

    More about this item

    Keywords

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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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