统计与金融计量Workshop

WORKSHOP预告

时间：2026年9月16日（周三）15:00-18:00

地点：暨南大学石牌校区经济学院大楼（中惠楼）323

主持人：朱海斌 暨南大学

报告摘要及专家简介

Carsten Chong

Carsten Chong在慕尼黑工业大学（Technical University of Munich）获得博士学位，师从 Claudia Klüppelberg 和 Jean Jacod。在瑞士洛桑联邦理工学院（EPFL）和哥伦比亚大学完成博士后研究后，他于2023 年秋季加入香港科技大学商学院信息系统、商业统计及营运学系（ISOM），担任助理教授。他目前的研究重点是利用高频收益数据及衍生品市场数据，对资产定价模型进行非参数估计。Carsten 现任Journal of Econometrics的副主编。

报告摘要

Return-based spot volatility estimates are noisy and prone to biases. This paper develops inference for the autocorrelation of intraday volatility changes that is robust to sampling errors in volatility estimates and biases due to jumps and microstructure noise. Our procedure builds on two results: First, we show that the limiting autocorrelation function of increments of a continuous-time process over shrinking horizons is parametrically determined and must coincide with that of fractional Gaussian noise. Second, we characterize the infill asymptotic distribution of realized autocovariances of spot variance estimators. The two results combine to yield a feasible generalized method of moments estimator of the autocorrelation of high-frequency volatility increments. In an empirical application to SPY transaction data, we document negative serial correlation in intraday movements of both latent and realized volatility.

丁一

丁一博士现任澳门大学工商管理学院商业经济学副教授。她在香港科技大学获得商业统计学博士学位，并在清华大学获得数学与应用数学学士学位。加入澳门大学之前，丁博士曾在香港理工大学应用数学系担任研究助理教授。她的研究主要集中在金融计量经济学、金融大数据以及高维统计学领域。丁博士已在The Annalsof Statistics,Journal of the American Statistical Association,Journal of Econometrics 等顶级学术期刊发表论文,并担任多个研究项日的负责人，这些项目获得了中国国家自然科学基金、香港研究资助局以及澳门科学技术发展基金的资助。

报告摘要

We study systematic trading activities at ultra-high frequencies and their implications for price formation. A continuous-time, high-dimensional factor model is proposed for stock trading volume. This model features a Common Trading Intensity (CTI) factor that captures common trading flow. We develop estimation and inference theories for studying the fine dynamics of CTI. Using trading volume data from U.S. equities, we find strong negative temporal dependence in short-horizon CTI movements, pointing to the presence of intraday systematic liquidity frictions. Moreover, large negative CTI shocks are followed by brief intraday price adjustments that unwind rapidly, suggesting efficient intraday systematic liquidity rebalancing.

李一帆

李一帆，现任英国曼彻斯特大学会计与金融系金融学高级讲师（副教授），并担任《Journal of Financial Econometrics》副主编。他的研究领域主要为金融计量经济学与计量经济学理论，研究兴趣包括高频金融计量、时间序列、期权数据分析，以及相关的金融应用。相关研究成果发表于 《Journal of Econometrics》《Journal of Financial Econometrics》《Journal of Economic Dynamics & Control》 等国际知名学术期刊。

报告摘要

We develop a new class of nonparametric spot correlation estimators based on high-frequency candlestick data comprising open, close, high, and low prices over short intraday time intervals. The new estimators augment the conventional return-based estimator with a signed asymmetry feature. We establish consistency and mixed normality in a traditional large-estimation-window in-fill asymptotic setting. We also develop a fixed-k asymptotic framework and confidence intervals that achieve near-nominal finite-sample coverage with very few candlesticks. Simulations further demonstrate substantial mean-squared-error reductions over return-based estimators and robustness to market microstructure noise. Relying on one-minute candlesticks, we find that FOMC announcements systematically raise stock-bond correlations, consistent with a discount-rate channel interpretation of the news, while President Trump's tariff-related posts affect more dispersed changes in the correlations. The empirical results highlight the value of the new candlestick-based spot estimators for capturing rapid changes in asset comovements around economic news and illuminating the economic transmission mechanisms at work.

校对| 朱海斌

责编| 彭毅

初审| 姜云卢

终审发布| 何凌云

（来源：暨南大学经济学院微信公众号）