arXiv 24 Apr 2026 · Econometrics
arXiv:2604.23023 · PDF · DOI · OpenAlex · Extracted main text
We develop a continuous-time penalized regression framework for the estimation of time-varying coefficients and variable selection when both the response and covariates are Itô semimartingales with jumps. The coefficient paths are approximated by spline basis expansions and estimated via least squares from truncated high-frequency increments. In a finite-dimensional setting, we establish consistency and derive a feasible asymptotic distribution for the integrated coefficient estimator under infill asymptotics. We then extend the framework to high-dimensional settings in which the number of candidate covariates diverges, and show that a group-wise penalized estimator with a truncated $\ell_1$-penalty attains the oracle property, which delivers both consistent model selection and coefficient estimation. An empirical application to a large panel of more than two hundred high-frequency factors documents sparse factor structure across a large cross-section of stocks and industry portfolios.
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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 | Aẗ-Sahalia, Y., Kalnina, I., and Xiu, D (2020) High-frequency factor models and regressions | 0.964 | 19 | 6 | 89% |
| 2 | Aleti, S (2023) The high-frequency factor zoo | 0.928 | 10 | 3 | 80% |
| 3 | Mykland, P. A. and Zhang, L (2009) Inference for continuous semimartingales observed at high frequency | 0.928 | 4 | 3 | 100% |
| 4 | Mancini, C (2009) Non-parametric threshold estimation for models with stochastic diffusion coefficient and jumps | 0.843 | 4 | 3 | 75% |
| 5 | Huang, J. Z., Wu, C. O., and Zhou, L (2004) Polynomial spline estimation and inference for varying coefficient models with longitudinal data | 0.843 | 5 | 3 | 60% |
| 6 | Xue, L. and Qu, A (2012) Variable selection in high-dimensional varying-coefficient models with global optimality | 0.737 | 3 | 3 | 67% |
| 7 | Fan, J. and Li, R (2001) Variable selection via nonconcave penalized likelihood and its oracle properties | 0.737 | 3 | 2 | 100% |
| 8 | Shen, X., Pan, W., and Zhu, Y (2012) Likelihood-based selection and sharp parameter estimation | 0.737 | 3 | 2 | 100% |
| 9 | de Boor, C (1978) A Practical Guide to Splines | 0.644 | 4 | 2 | 50% |
| 10 | Bach, F. R (2008) Consistency of the group Lasso and multiple kernel learning | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 86 scored citations.