Corpus ID: 235489725

Residual-Based Nodewise Regression in Factor Models with Ultra-High Dimensions: Analysis of Mean-Variance Portfolio Efficiency and Estimation of Out-of-Sample and Constrained Maximum Sharpe Ratios

@inproceedings{Caner2020ResidualBasedNR,
  title={Residual-Based Nodewise Regression in Factor Models with Ultra-High Dimensions: Analysis of Mean-Variance Portfolio Efficiency and Estimation of Out-of-Sample and Constrained Maximum Sharpe Ratios},
  author={Mehmet Caner and Marcelo C. Medeiros and Gabriel F. R. Vasconcelos},
  year={2020}
}
We provide a new theory for nodewise regression when the residuals from a fitted factor model are used to apply our results to the analysis of the maximum Sharpe ratio when the number of assets in a portfolio is larger than its time span. We introduce a new hybrid model where factor models are combined with feasible nodewise regression. Returns are generated from an increasing number of factors plus idiosyncratic components (errors). The precision matrix of the idiosyncratic terms is assumed to… Expand

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