Forecasting large realized covariance matrices: the benefits of factor models and shrinkage

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e 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.

Titulo de periódico
Journal of Financial Econometrics
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en
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Membros da banca
Área do Conhecimento CNPQ
CIENCIAS SOCIAIS APLICADAS
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