arXiv 14 Oct 2025 · Econometrics
arXiv:2510.12911 · PDF · DOI · OpenAlex · Extracted main text
Spot covariance estimation is commonly based on high-frequency open-to-close return data over short time windows, but such approaches face a trade-off between statistical accuracy and localization. In this paper, I introduce a new estimation framework using high-frequency candlestick data, which include open, high, low, and close prices, effectively addressing this trade-off. By exploiting the information contained in candlesticks, the proposed method improves estimation accuracy relative to benchmarks while preserving local structure. I further develop a test for spot covariance inference based on candlesticks that demonstrates reasonable size control and a notable increase in power, particularly in small samples. Motivated by recent work in the finance literature, I empirically test the market neutrality of the iShares Bitcoin Trust ETF (IBIT) using 1-minute candlestick data for the full year of 2024. The results show systematic deviations from market neutrality, especially in periods of market stress. An event study around FOMC announcements further illustrates the new method's ability to detect subtle shifts in response to relatively mild information events.
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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 | Bollerslev, Tim and Li, Jia and Ren, Yuexuan (2024) Optimal inference for spot regressions | 1.000 | 8 | 4 | 100% |
| 2 | Li, Jia and Wang, Dishen and Zhang, Qiushi (2024) Reading the candlesticks: An OK estimator for volatility | 1.000 | 7 | 5 | 100% |
| 3 | Bollerslev, Tim and Li, Jia and Li, Qiyuan (2024) Optimal nonparametric range-based volatility estimation | 1.000 | 6 | 4 | 100% |
| 4 | Bollerslev, Tim and Li, Jia and Liao, Zhipeng (2021) Fixed-k inference for volatility | 0.843 | 5 | 3 | 60% |
| 5 | Jacod, Jean and Protter, Philip (2012) Discretization of Processes | 0.737 | 3 | 3 | 67% |
| 6 | A\"it-Sahalia, Yacine and Jacod, Jean (2014) High-frequency financial econometrics | 0.737 | 3 | 2 | 100% |
| 7 | Liu, Yukun and Tsyvinski, Aleh (2021) Risks and returns of cryptocurrency | 0.737 | 3 | 2 | 100% |
| 8 | Rogers, Leonard CG and Zhou, Fanyin (2008) Estimating correlation from high, low, opening and closing prices | 0.737 | 3 | 2 | 100% |
| 9 | Jacod, Jean and Li, Jia and Liao, Zhipeng (2021) Volatility coupling | 0.644 | 3 | 2 | 67% |
| 10 | Bollerslev, Tim and Li, Jia and Li, Qiyuan and Li, Yifan (2025) Optimal Candlestick-Based Spot Volatility Estimation: New Tricks and Feasible Inference Procedures | 0.644 | 2 | 2 | 100% |
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