阅读烛台:波动率的确定估算器

Jia Li, Dishen Wang, Qiushi Zhang
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引用次数: 0

摘要

我们提出了一种利用高频蜡烛图观测的现货波动率最优蜡烛图 K(OK)估计器。在标准填充渐近设置下,我们证明 OK 估计器在一类线性估计器中是渐近无偏的,并且具有最小的渐近方差。其估计误差可由布朗函数耦合,从而实现有效推断。我们的理论和数值结果表明,所提出的基于蜡烛图的估计器要比基于高频回报的传统现货波动率估计器准确得多。在美联储主席最近的国会证词中,一个经验性例证记录了各种资产的盘中波动动态。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reading the Candlesticks: An OK Estimator for Volatility

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.

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