Sung Hoon Choi, Donggyu Kim
arXiv 5 Mar 2024 · Econometrics · publishedJournal of Business and Economic Statistics (2025) · 1 citations (OpenAlex)
arXiv:2403.02591 · PDF · DOI · OpenAlex · Extracted main text
In this paper, we introduce a novel method for predicting intraday instantaneous volatility based on Ito semimartingale models using high-frequency financial data. Several studies have highlighted stylized volatility time series features, such as interday auto-regressive dynamics and the intraday U-shaped pattern. To accommodate these volatility features, we propose an interday-by-intraday instantaneous volatility matrix process that can be decomposed into low-rank conditional expected instantaneous volatility and noise matrices. To predict the low-rank conditional expected instantaneous volatility matrix, we propose the Two-sIde Projected-PCA (TIP-PCA) procedure. We establish asymptotic properties of the proposed estimators and conduct a simulation study to assess the finite sample performance of the proposed prediction method. Finally, we apply the TIP-PCA method to an out-of-sample instantaneous volatility vector prediction study using high-frequency data from the S&P 500 index and 11 sector index funds.
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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 | Fan, J., Y. Liao, and W. Wang (2016) b): Projected principal component analysis in factor models | 0.928 | 5 | 3 | 80% |
| 3 | Corsi, F (2009) A simple approximate long-memory model of realized volatility | 0.928 | 4 | 3 | 100% |
| 4 | Ahn, S. C. and A. R. Horenstein (2013) Eigenvalue ratio test for the number of factors | 0.737 | 3 | 2 | 100% |
| 5 | Cho, J., D. Kim, and K. Rohe (2017) Asymptotic theory for estimating the singular vectors and values of a partially-observed low rank matrix with noise | 0.737 | 3 | 2 | 100% |
| 6 | Zhang, C., Y. Zhang, M. Cucuringu, and Z. Qian (2024) Volatility forecasting with machine learning and intraday commonality | 0.737 | 3 | 2 | 100% |
| 7 | Admati, A. R. and P. Pfleiderer (1988) A theory of intraday patterns: Volume and price variability | 0.644 | 2 | 2 | 100% |
| 8 | Andersen, T. G. and T. Bollerslev (1997) Intraday periodicity and volatility persistence in financial markets | 0.644 | 2 | 2 | 100% |
| 9 | Andersen, T. G., M. Thyrsgaard, and V. Todorov (2019) Time-varying periodicity in intraday volatility | 0.644 | 2 | 2 | 100% |
| 10 | Fan, J. and Y. Wang (2008) Spot volatility estimation for high-frequency data | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 57 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Low-Rank Structured Nonparametric Prediction of Instantaneous Volatility | 1.000 | 5 | 3 |