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.

翻译：瞬时协方差估计通常基于短时间窗口内的高频开盘至收盘收益率数据，但此类方法在统计精度与局部化之间存在权衡。本文提出一种利用高频K线数据（包含开盘价、最高价、最低价和收盘价）的新估计框架，有效解决了这一权衡问题。通过挖掘K线图所蕴含的信息，所提方法在保持局部结构的同时，相较于基准方法提升了估计精度。本文进一步构建了一种基于K线图的瞬时协方差推断检验，该检验展现出合理的尺寸控制能力，并在小样本情况下显著提高了检验功效。受近期金融文献研究的启发，我使用2024年全年的1分钟K线数据，对iShares比特币信托ETF（IBIT）的市场中性属性进行了实证检验。结果显示其存在系统性偏离市场中性的现象，在市场压力时期尤为明显。围绕联邦公开市场委员会（FOMC）公告的事件研究进一步表明，新方法能够检测出相对温和的信息事件所引发的细微市场反应变化。