Tae-Hwy Lee, Ekaterina Seregina
arXiv 1 Nov 2020 · Econometrics · publishedJournal of Financial Econometrics (2023) · 8 citations (OpenAlex)
arXiv:2011.00435 · PDF · DOI · OpenAlex · Extracted main text
Graphical models are a powerful tool to estimate a high-dimensional inverse covariance (precision) matrix, which has been applied for a portfolio allocation problem. The assumption made by these models is a sparsity of the precision matrix. However, when stock returns are driven by common factors, such assumption does not hold. We address this limitation and develop a framework, Factor Graphical Lasso (FGL), which integrates graphical models with the factor structure in the context of portfolio allocation by decomposing a precision matrix into low-rank and sparse components. Our theoretical results and simulations show that FGL consistently estimates the portfolio weights and risk exposure and also that FGL is robust to heavy-tailed distributions which makes our method suitable for financial applications. FGL-based portfolios are shown to exhibit superior performance over several prominent competitors including equal-weighted and Index portfolios in the empirical application for the S&P500 constituents.
appendix boundary found by appendix_command · 44% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Callot, L., Caner, M., Önder, A. O., and Ulaşan, E (2021) A nodewise regression approach to estimating large portfolios | 1.000 | 7 | 3 | 100% |
| 2 | Fan, J., Liao, Y., and Mincheva, M (2013) Large covariance estimation by thresholding principal orthogonal complements | 0.974 | 13 | 5 | 92% |
| 3 | Koike, Y (2020) De-biased graphical lasso for high-frequency data | 0.950 | 7 | 4 | 86% |
| 4 | Bai, J (2003) Inferential theory for factor models of large dimensions | 0.874 | 5 | 2 | 100% |
| 5 | Friedman, J., Hastie, T., and Tibshirani, R (2008) Sparse inverse covariance estimation with the Graphical Lasso | 0.860 | 11 | 4 | 64% |
| 6 | Cai, T., Liu, W., and Luo, X (2011) A constrained l1-minimization approach to sparse precision matrix estimation | 0.811 | 4 | 2 | 100% |
| 7 | Fan, J., Liu, H., and Wang, W (2018) Large covariance estimation through elliptical factor models | 0.794 | 12 | 6 | 50% |
| 8 | Janková, J. and van de Geer, S (2018) Inference in high-dimensional graphical models | 0.737 | 4 | 4 | 50% |
| 9 | Onatski, A (2013) Discussion on the paper by Fan J., Liao Y., and Mincheva M. Large covariance estimation by thresholding principal orthogonal com… | 0.737 | 3 | 3 | 67% |
| 10 | Fan, J., Liao, Y., and Mincheva, M (2011) High-dimensional covariance matrix estimation in approximate factor models | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 50 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Combining Forecasts under Structural Breaks Using Graphical LASSO | 1.000 | 7 | 3 |
| 2 | Learning from Forecast Errors: A New Approach to Forecast Combinations | 0.644 | 2 | 2 |
| 3 | Inferential Theory for Granular Instrumental Variables in High Dimensions | 0.000 | 2 | 1 |