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22 mars 2023
- 2303.16151
Mots-clés
Quantitative Finance - Statistical Finance Computer Science - Machine Learning Economics - Econometrics Statistics - Machine LearningSujets proches
Matrix algebra Algebra, Matrix Matrixes (Algebra) Cracovians (Mathematics) Learning, Machine Prediction Forecasts Futurology Funds Funding Pattern Model Finance, commerce, confiscations, etcCiter ce document
Rafael Alves et al., « Forecasting Large Realized Covariance Matrices: The Benefits of Factor Models and Shrinkage », arXiv - économie
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Résumé
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 (VHAR) models with the least absolute shrinkage and selection operator (LASSO). Our methodology improves forecasting precision relative to standard benchmarks and leads to better estimates of minimum variance portfolios.