Skip to content
DraconiticResearch
Search Sources API
203,234 studies31 sourcesUpdated 3 min ago
PreprintarXiv licencearXiv

Functional Inference for the Mean Curve of Intraday Spot Correlation

  • Aodong Guo
  • Yu Jiang
  • Zhi Liu
Open at sourcePDFDOICite

Abstract

In this paper, we propose estimators for the mean of the daily spot-correlation curves, which are robust to the presence of finite-variation jumps in the price processes. The related feasible functional central limit theorems for the proposed estimators have been established under a joint infill and long-span setting. Based on the asymptotic theory, we develop a test procedure for flatness of the curves. Monte Carlo simulations confirm the strong finite-sample performance of the estimators. In an empirical application to 30 U.S. stocks from 2010 to 2019, we find a pronounced end-of-day surge in the mean of correlation curves across diverse stock groupings, alongside substantial calendar-time non-flatness in the curves.

Related

  1. Finite-rank rigidity and Nash-curve discreteness for correlation potentials
    Josiah Park
    arXivPreprintCC BY-SA 4.0Full textAlgebra & number theoryAnalysis & PDEsarXiv
  2. The correlation energy of the electron gas in the mean-field regime
    Martin Ravn Christiansen, Christian Hainzl, Phan Thành Nam
    Journal of the American Mathematical SocietyPeer-reviewedLicence not statedFull text15 citedMathematical physicsOpenAlex
  3. The mean distance to a simple closed curve on the sphere
    J. V. M. Pimentel
    arXivPreprintCC BY 4.0Full textGeometry & topologyarXiv
  4. Selective Inference in Growth Curve Models
    Haochen Lei, Qian Zhang, Hongyuan Cao
    arXivPreprintCC BY 4.0Full textStatisticsarXiv
  5. Inference for Fréchet Mean Sets
    Ryunosuke Miyazaki
    arXivPreprintarXiv licenceFull textStatisticsarXiv

Cite

BibTeXRIS
BibTeX
@misc{guo2026functional,
  title = {{Functional Inference for the Mean Curve of Intraday Spot Correlation}},
  author = {Aodong Guo and Yu Jiang and Zhi Liu},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.31208},
  eprint = {2610.31208},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.31208},
}
RIS
TY  - UNPB
TI  - Functional Inference for the Mean Curve of Intraday Spot Correlation
AU  - Aodong Guo
AU  - Yu Jiang
AU  - Zhi Liu
PY  - 2026
DA  - 2026/10/22
PB  - arXiv
DO  - 10.48550/arxiv.2610.31208
UR  - https://arxiv.org/abs/2610.31208
AB  - In this paper, we propose estimators for the mean of the daily spot-correlation curves, which are robust to the presence of finite-variation jumps in the price processes. The related feasible functional central limit theorems for the proposed estimators have been established under a joint infill and long-span setting. Based on the asymptotic theory, we develop a test procedure for flatness of the curves. Monte Carlo simulations confirm the strong finite-sample performance of the estimators. In an empirical application to 30 U.S. stocks from 2010 to 2019, we find a pronounced end-of-day surge in the mean of correlation curves across diverse stock groupings, alongside substantial calendar-time non-flatness in the curves.
ER  -