Reading the Candlesticks: An OK Estimator for Volatility

• 成果类型: Article
• 署名作者: Li, Jia; Wang, Dishen; Zhang, Qiushi
• 署名单位: Singapore Management University; University of International Business & Economics
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/rest_a_01203
• 发表日期: 2024-07
• 页码: 1114-1128
• 关键词: range-based estimation; microstructure noise; variance; inference; models

摘要

We propose an Optimal candlesticK (OK) estimator for the spot volatility using high-frequency candlestick observations. Under a standard infill asymptotic setting, we show that the OK estimator is asymptotically unbiased and has minimal asymptotic variance within a class of linear estimators. Its estimation error can be coupled by a Brownian functional, which permits valid inference. Our theoretical and numerical results suggest that the proposed candlestick-based estimator is much more accurate than the conventional spot volatility estimator based on high-frequency returns. An empirical illustration documents the intraday volatility dynamics of various assets during the Fed chairman's recent congressional testimony.