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Forecasting Large Realized Covariance Matrices: The Benefits of Factor Models and Shrinkage

Author

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  • Rafael P Alves
  • Diego S de Brito
  • Marcelo C Medeiros
  • Ruy M Ribeiro

Abstract

We propose a model to forecast large realized covariance matrices of returns, applying it to the constituents of the S&P 500 daily. To address the curse of dimensionality, we decompose the return covariance matrix using standard firm-level factors (e.g., size, value, and profitability) and use sectoral restrictions in the residual covariance matrix. This restricted model is then estimated using vector heterogeneous autoregressive models with the least absolute shrinkage and selection operator. Our methodology improves forecasting precision relative to standard benchmarks and leads to better estimates of minimum variance portfolios.

Suggested Citation

  • Rafael P Alves & Diego S de Brito & Marcelo C Medeiros & Ruy M Ribeiro, 2024. "Forecasting Large Realized Covariance Matrices: The Benefits of Factor Models and Shrinkage," Journal of Financial Econometrics, Oxford University Press, vol. 22(3), pages 696-742.
    • Handle: RePEc:oup:jfinec:v:22:y:2024:i:3:p:696-742.
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbad013
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    More about this item

    Keywords

    big data; factor models; forecasting; LASSO; machine learning; portfolio allocation; realized covariance; shrinkage;
    All these keywords.

    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
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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