Reading the candlesticks: An OK estimator for volatility

• Main Authors: LI, Jia, WANG, Dishen, ZHANG, Qiushi
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Range-based estimation; microstructure noise; inference; Semimartingale; Volatility; Econometrics
• Online Access: https://ink.library.smu.edu.sg/soe_research/2565; https://ink.library.smu.edu.sg/context/soe_research/article/3564/viewcontent/Reading_the_Candlesticks.pdf
• Institution: Singapore Management University

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.