arXiv 29 Jul 2025 · Econometrics
arXiv:2507.22173 · PDF · DOI · OpenAlex · Extracted main text
Based on It\^o semimartingale models, several studies have proposed methods for forecasting intraday volatility using high-frequency financial data. These approaches typically rely on restrictive parametric assumptions and are often vulnerable to model misspecification. To address this issue, we introduce a novel nonparametric prediction method for the future intraday instantaneous volatility process during trading hours, which leverages both previous days' data and the current day's observed intraday data. Our approach imposes an interday-by-intraday matrix representation of the instantaneous volatility, which is decomposed into a low-rank conditional expectation component and a noise matrix. To predict the future conditional expected volatility vector, we exploit this low-rank structure and propose the Structural Intraday-volatility Prediction (SIP) procedure. We establish the asymptotic properties of the SIP estimator and demonstrate its effectiveness through an out-of-sample prediction study using real high-frequency trading data.
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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 | Figueroa-López, J. E. and B. Wu (2024) Kernel estimation of spot volatility with microstructure noise using pre-averaging | 1.000 | 6 | 4 | 100% |
| 2 | Choi, S. H. and D. Kim (2025) Matrix-based prediction approach for intraday instantaneous volatility vector self | 1.000 | 5 | 3 | 100% |
| 3 | Ahn, S. C. and A. R. Horenstein (2013) Eigenvalue ratio test for the number of factors | 0.737 | 3 | 2 | 100% |
| 4 | Zhang, C., Y. Zhang, M. Cucuringu, and Z. Qian (2024) Volatility forecasting with machine learning and intraday commonality | 0.737 | 3 | 2 | 100% |
| 5 | Candes, E. J. and Y. Plan (2010) Matrix completion with noise | 0.644 | 2 | 2 | 100% |
| 6 | Corsi, F (2009) A simple approximate long-memory model of realized volatility | 0.644 | 2 | 2 | 100% |
| 7 | Fan, J. and Y. Wang (2008) Spot volatility estimation for high-frequency data | 0.644 | 2 | 2 | 100% |
| 8 | Fan, J. and D. Kim (2018) Robust high-dimensional volatility matrix estimation for high-frequency factor model | 0.644 | 2 | 2 | 100% |
| 9 | Foster, D. P. and D. B. Nelson (1996) Continuous record asymptotics for rolling sample variance estimators | 0.644 | 2 | 2 | 100% |
| 10 | Kristensen, D (2010) Nonparametric filtering of the realized spot volatility: A kernel-based approach | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 64 scored citations.