Jialing Han, Yu-Ning Li
arXiv 15 Aug 2025 · Statistics — Methodology
arXiv:2508.11619 · PDF · Extracted main text
We propose a novel framework for approximate factor models that integrates an S-vine copula structure to capture complex dependencies among common factors. Our estimation procedure proceeds in two steps: first, we apply principal component analysis (PCA) to extract the factors; second, we employ maximum likelihood estimation that combines kernel density estimation for the margins with an S-vine copula to model the dependence structure. Jointly fitting the S-vine copula with the margins yields an oblique factor rotation without resorting to ad hoc restrictions or traditional projection pursuit methods. Our theoretical contributions include establishing the consistency of the rotation and copula parameter estimators, developing asymptotic theory for the factor-projected empirical process under dependent data, and proving the uniform consistency of the projected entropy estimators. Simulation studies demonstrate convergence with respect to both the dimensionality and the sample size. We further assess model performance through Value-at-Risk (VaR) estimation via Monte Carlo methods and apply our methodology to the daily returns of S&P 500 Index constituents to forecast the VaR of S&P 500 index.
appendix boundary found by appendix_command · 53% 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 | Bai, J., Ng, S (2002) Determining the number of factors in approximate factor models | 1.000 | 5 | 4 | 100% |
| 2 | Nagler, T., Krüger, D., Min, A (2022) Stationary vine copula models for multivariate time series | 0.909 | 12 | 6 | 75% |
| 3 | Beare, B.K., Seo, J (2015) Vine copula specifications for stationary multivariate markov chains | 0.843 | 3 | 3 | 100% |
| 4 | Hafner, C.M., Herwartz, H., Wang, S (2025) Statistical identification of independent shocks with kernel-based maximum likelihood estimation and an application to the globa… | 0.737 | 4 | 4 | 50% |
| 5 | Li, Y.N., Li, D., Fryzlewicz, P (2023) Detection of multiple structural breaks in large covariance matrices self | 0.737 | 4 | 3 | 50% |
| 6 | Kong, X., Wang, J., Xing, J., Xu, C., Ying, C (2019) Factor and idiosyncratic empirical processes | 0.737 | 3 | 3 | 67% |
| 7 | Fan, J., Liao, Y., Mincheva, M (2013) Large covariance estimation by thresholding principal orthogonal complements | 0.737 | 3 | 2 | 100% |
| 8 | Aas, K., Czado, C., Frigessi, A., Bakken, H (2009) Pair-copula constructions of multiple dependence | 0.644 | 2 | 2 | 100% |
| 9 | Chen, X., Fan, Y (2006) Estimation of copula-based semiparametric time series models | 0.511 | 2 | 1 | 100% |
| 10 | Chen, J., Li, D., Li, Y.N., Linton, O (2025) Estimating time-varying networks for high-dimensional time series self | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 64 scored citations.