arXiv 7 Dec 2024 · Econometrics · 1 citations (OpenAlex)
arXiv:2412.05664 · PDF · DOI · OpenAlex · Extracted main text
In financial applications, we often observe both global and local factors that are modeled by a multi-level factor model. When detecting unknown local group memberships under such a model, employing a covariance matrix as an adjacency matrix for local group memberships is inadequate due to the predominant effect of global factors. Thus, to detect a local group structure more effectively, this study introduces an inverse covariance matrix-based financial adjacency matrix (IFAM) that utilizes negative values of the inverse covariance matrix. We show that IFAM ensures that the edge density between different groups vanishes, while that within the same group remains non-vanishing. This reduces falsely detected connections and helps identify local group membership accurately. To estimate IFAM under the multi-level factor model, we introduce a factor-adjusted GLASSO estimator to address the prevalent global factor effect in the inverse covariance matrix. An empirical study using returns from international stocks across 20 financial markets demonstrates that incorporating IFAM effectively detects latent local groups, which helps improve the minimum variance portfolio allocation performance.
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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 | Friedman, J., Hastie, T., and Tibshirani, R (2008) Sparse inverse covariance estimation with the graphical lasso | 1.000 | 5 | 3 | 100% |
| 2 | Qin, T. and Rohe, K (2013) Regularized spectral clustering under the degree-corrected stochastic blockmodel | 0.941 | 6 | 4 | 83% |
| 3 | Ahn, S. C. and Horenstein, A. R (2013) Eigenvalue ratio test for the number of factors | 0.928 | 4 | 3 | 100% |
| 4 | Choi, S. H. and Kim, D (2023) Large volatility matrix analysis using global and national factor models self | 0.888 | 10 | 4 | 70% |
| 5 | Fan, J., Liao, Y., and Mincheva, M (2013) Large covariance estimation by thresholding principal orthogonal complements | 0.737 | 3 | 2 | 100% |
| 6 | Ravikumar, P., Wainwright, M. J., Raskutti, G., and Yu, B (2011) High-dimensional covariance estimation by minimizing $_1$-penalized log-determinant divergence | 0.693 | 8 | 2 | 50% |
| 7 | Cai, T., Liu, W., and Luo, X (2011) A constrained $_ 1$ minimization approach to sparse precision matrix estimation | 0.644 | 2 | 2 | 100% |
| 8 | Fan, J., Furger, A., and Xiu, D (2016) Incorporating global industrial classification standard into portfolio allocation: A simple factor-based large covariance matrix… | 0.644 | 2 | 2 | 100% |
| 9 | Meinshausen, N. and Bühlmann, P (2006) High-dimensional graphs and variable selection with the Lasso | 0.644 | 2 | 2 | 100% |
| 10 | Mossel, E., Neeman, J., and Sly, A (2015) Reconstruction and estimation in the planted partition model | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 90 scored citations.