2023/03/22 by Rafael Alves, Diego S. de Brito, Alves, Rafael +5
Decision Sciences · Economics, Econometrics and Finance · #Complex Systems and Time Series Analysis #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Financial Markets and Investment Strategies #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Statistical Finance (q-fin.ST) #Stock Market Forecasting Methods
paper · pdf · doi:10.48550/arxiv.2303.16151
openalex publication_date 2023/03/22 · openalex created_date 2023/03/31 · openalex updated_date 2026/07/28
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.