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sidenav header background[4月8日] 计量金融与大数据分析workshop
发布日期：2022-04-02 10:59 来源：
Reading the Candlesticks: An OK Estimator for Volatility
解读股票蜡烛：基于高频数据的OK波动率估计量
主讲人：张秋诗（对外经济贸易大学）
主持人：（国发院）沈艳、黄卓、孙振庭、张俊妮
（北大新结构经济学研究院）胡博
（经济学院）王一鸣、王熙、刘蕴霆
时间：2022年4月8日（周五）10:00 AM -- 11:30 AM
地点：经院107
报告摘要：
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.
主讲人简介：
张秋诗，对外经济贸易大学金融工程系助理教授。2018年于 美国杜克大学获得经济学博士学位，2017年于美国纽约大学获得数学、经济双荣誉学士学位。研究兴趣是计量经济学，金融经济学，行为金融学，金融衍生品。担任Journal of Business & Economic Statistics, Journal of Financial Econometrics匿名审稿人。论文著作包括“Testing the Dimensionality of Policy Shocks” (with Jia Li and Viktor Todorov) Review of Economics and Statistics, forthcoming. “Reading the Candlesticks: An OK Estimator for Volatility” (with Jia Li and Dishen Wang) Review of Economics and Statistics, accepted. “Nonlinear and Related Panel Data Models” (with William Greene) Chapter 3 in Panel Data Econometrics, M. Tsionas, ed., Academic Press, 2019.
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首页
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教学项目展开 / 收起
sidenav background教学项目
sidenav header background[4月8日] 计量金融与大数据分析workshop
发布日期：2022-04-02 10:59 来源：
Reading the Candlesticks: An OK Estimator for Volatility
解读股票蜡烛：基于高频数据的OK波动率估计量
主讲人：张秋诗（对外经济贸易大学）
主持人：（国发院）沈艳、黄卓、孙振庭、张俊妮
（北大新结构经济学研究院）胡博
（经济学院）王一鸣、王熙、刘蕴霆
时间：2022年4月8日（周五）10:00 AM -- 11:30 AM
地点：经院107
报告摘要：
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.
主讲人简介：
张秋诗，对外经济贸易大学金融工程系助理教授。2018年于 美国杜克大学获得经济学博士学位，2017年于美国纽约大学获得数学、经济双荣誉学士学位。研究兴趣是计量经济学，金融经济学，行为金融学，金融衍生品。担任Journal of Business & Economic Statistics, Journal of Financial Econometrics匿名审稿人。论文著作包括“Testing the Dimensionality of Policy Shocks” (with Jia Li and Viktor Todorov) Review of Economics and Statistics, forthcoming. “Reading the Candlesticks: An OK Estimator for Volatility” (with Jia Li and Dishen Wang) Review of Economics and Statistics, accepted. “Nonlinear and Related Panel Data Models” (with William Greene) Chapter 3 in Panel Data Econometrics, M. Tsionas, ed., Academic Press, 2019.
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