Betas from spot regressions are central to asset pricing and risk management, as measures of systematic risk. This paper develops a new estimation and inference framework for spot regressions by leveraging high-frequency candlesticks, extending conventional (open-to-close) returns with intra-period high/low prices. Specifically, I construct candlestick-based estimators of regression parameters, including spot beta, by minimizing a quadratic risk under a fixed-k asymptotic framework. I then develop a feasible hypothesis testing procedure for spot betas with correct asymptotic size. Simulation results show that the proposed estimator reduces estimation risk relative to return-based estimators, especially in small samples, and the test achieves notably higher power. I apply the framework to assess the market neutrality of Bitcoin using 1-minute data on IBIT and SPY, finding deviations from neutrality, particularly in high-volatility periods.

翻译：点回归中的贝塔系数作为系统性风险的度量，在资产定价与风险管理中占据核心地位。本文通过利用高频K线图数据（将期初至期末的传统收益率扩展为包含期内最高/最低价格信息）构建了点回归的全新估计与推断框架。具体而言，作者在固定K渐近分析框架下，通过最小化二次风险构造了基于K线图的回归参数（包括点贝塔）估计量，并开发了具有正确渐近规模的可行点贝塔假设检验方法。模拟结果表明，相较于基于收益率的估计方法，本文提出的估计量能显著降低估计风险（尤其是在小样本场景下），且检验统计量具有更高的检验功效。本文应用该框架，基于IBIT与SPY的一分钟高频数据评估比特币的市场中性特征，发现其存在显著的市场中性偏离，特别是在高波动时期尤为明显。