Associate Professor of Econometrics and Statistics
Booth School of Business, University of Chicago

5807 South Woodlawn Avenue, Chicago, IL 60637
Google Scholar Profile
Curriculum Vitae

Dacheng Xiu’s research interests include developing statistical methodologies and applying them to financial data, while exploring their economic implications. His earlier research involved risk measurement and portfolio management with high-frequency data and econometric modeling of derivatives. His current work focuses on developing machine learning solutions to big-data problems in empirical asset pricing.

Xiu’s work has appeared in Econometrica, the Journal of Econometrics, the Journal of the American Statistical Association, the Annals of Statistics, and the Journal of Business & Economic Statistics. He is an Associate Editor for the Journal of Econometrics and Statistica Sinica, and also referees for several journals in the fields of econometrics, statistics, and finance. He has received several recognitions for his research, including the Dennis J. Aigner 2017 Honorable Mention for the best paper in empirical econometrics published by the Journal of Econometrics in 2015 or 2016, and the Best Conference Paper Prize at the 2017 Annual Meeting of the European Finance Association. In 2017, Xiu launched a website that provides up-to-date realized volatilities of individual stocks. These daily volatilities are calculated from the stocks’ intraday transactions and the methodologies are based on his research of high-frequency data.

Xiu earned his PhD and MA in applied mathematics from Princeton University, where he was also a researcher at the Bendheim Center for Finance. Prior to his graduate studies, he obtained a BS in mathematics from the University of Science and Technology of China.
Factor Model Covariance Estimation Microstructure Noise MSCI Barra Model PCA Portfolio Allocation Big Data
"Efficient Estimation of Integrated Volatility Functionals via Multiscale Jackknife", with Jia Li and Yunxiao Liu, forthcoming in the Annals of Statistics.
Spot (Co)Variance Jackknife Bootstrap
"Principal Component Analysis of High Frequency Data", with Yacine Aït-Sahalia, forthcoming in the Journal of the American Statistical Association.
Biplots.mp4 PCA Spot (Co)Variance
"A Hausman Test for the Presence of Market Microstructure Noise in High Frequency Data", with Yacine Aït-Sahalia, forthcoming in the Journal of Econometrics.
Matlab Codes.zip Microstructure Noise QMLE Volatility Estimation
"Resolution of Policy Uncertainty and Sudden Declines in Volatility", with Dante Amengual, Journal of Econometrics 203 (2018), 297-315.
Supplemental Material.pdf Chicago Booth Review   Variance Swaps Pricing Non-affine Models Downward Volatility Jumps VIX
Matlab Codes.zip QMLE Factor Model Covariance Estimation Microstructure Noise Kalman Filtering, Smoothing, and EM Algorithm
"Nonparametric Estimation of the Leverage Effect: A Trade-off between Robustness and Efficiency", with Ilze Kalnina, Journal of the American Statistical Association 112 (2017), 384-396.
Covariance Estimation VIX Spot (Co)Variance
"Using Principal Component Analysis to Estimate a High Dimensional Factor Model with High-Frequency Data", with Yacine Aït-Sahalia, Journal of Econometrics 201 (2017), 384-399.
High Frequency Fama-French Factors Big Data Factor Model PCA Portfolio Allocation Covariance Estimation
"Increased Correlation Among Asset Classes: Are Volatility or Jumps to Blame, or Both?" , with Yacine Aït-Sahalia, Journal of Econometrics 194 (2016), 205-219.
Co-Jumps Covariance Estimation Microstructure Noise
"A Tale of Two Option Markets: Pricing Kernels and Volatility Risk", with Zhaogang Song, Journal of Econometrics 190 (2016), 176-196. Honorable mention of the 2017 Dennis J. Aigner Award.
Nonparametric Option Pricing Closed-form Pricing of SPX and VIX Options Particle Filtering
"Quasi-Maximum Likelihood Estimation of GARCH Models with Heavy-Tailed Likelihoods", with Jianqing Fan and Lei Qi, Journal of Business & Economic Statistics 32 (2014), 178-191. Invited Paper with Discussion.
Rejoinder.pdf Matlab Codes.zip Volatility Estimation
"Hermite Polynomial Based Expansion of European Option Prices", Journal of Econometrics 179 (2014), 158-177.
Option Pricing Non-affine Models
"High-Frequency Covariance Estimates with Noisy and Asynchronous Data", with Yacine Aït-Sahalia and Jianqing Fan, Journal of the American Statistical Association 105 (2010), 1504-1517.
Matlab Codes.zip QMLE Covariance Estimation Microstructure Noise
QMLE Volatility Estimation Microstructure Noise
Supplemental Material.pdf QMLE Volatility Estimation Microstructure Noise Kalman Filtering Model Selection
"Taming the Factor Zoo", with Guanhao Feng and Stefano Giglio, Aug. 2017. In Revision. Finalist of the 2018 AQR Insight Award.
Slides Machine Learning Model Selection Cross-Section of Expected Returns Big Data
"Inference on Risk Premia in the Presence of Omitted Factors", with Stefano Giglio, Jan. 2017. In Revision. Winner of the Best Conference Paper Prize at the 44th EFA.
Chicago Booth Review   Slides Matlab Codes.zip Three-Pass Estimator Factor Model PCA Cross-Section of Expected Returns Big Data
"Empirical Asset Pricing via Machine Learning", with Shihao Gu and Bryan Kelly, Apr. 2018.
Machine Learning Time-Series and Cross-Section of Expected Returns Big Data Return Predictability
"Comment on: Limit of Random Measures Associated with the Increments of a Brownian Semimartingale", with Jia Li, forthcoming in the Journal of Financial Econometrics.
"Likelihood-Based Volatility Estimators in the Presence of Market Microstructure Noise: A Review", with Yacine Aït-Sahalia, Handbook of Volatility Models and their Applications, Chapter 14, Wiley 2012.
The 2008 meltdown of the financial system has led to tremendous interest in understanding and controlling the systemic risk of the financial market, which further hinges on the assessment of the risks of individual assets on the market and their interdependencies. The increasing availability of transaction-level data on a growing cross-section of tradable assets presents a unique opportunity and yet substantial challenges in estimating these quantities. The overarching theme of the lab is to design methodologies that exploit information embedded in the big data to better ascertain and manage the risk.
Realized Volatility Beta 1.0
Add RV
Sector ETFs04/20/201804/19/201804/18/201804/17/201804/16/2018
MaterialXLB12.54% ±1.1%13.57% ±1.1%10.29% ±0.9%10.07% ±0.9%11.07% ±0.9%
IndustrialXLI13.17% ±0.9%13.79% ±0.7%09.88% ±0.6%10.27% ±0.7%12.39% ±0.8%
Consumer DiscretionaryXLY12.18% ±0.8%11.46% ±0.8%09.36% ±0.7%09.20% ±0.7%10.82% ±0.8%
Consumer StaplesXLP11.21% ±0.7%15.05% ±0.9%08.21% ±0.6%08.81% ±0.7%10.76% ±0.9%
Health CareXLV12.04% ±0.8%13.51% ±0.8%10.13% ±0.7%10.86% ±0.7%11.84% ±0.7%
FinancialXLF15.35% ±0.9%15.82% ±0.7%13.23% ±0.6%13.57% ±0.6%16.76% ±0.7%
TechnologyXLK14.54% ±0.9%14.35% ±0.9%10.78% ±0.7%09.60% ±0.6%13.00% ±0.8%
UtilitiesXLU11.02% ±0.6%12.16% ±0.8%10.10% ±0.7%09.84% ±0.7%12.37% ±0.8%
EnergyXLE16.21% ±0.8%16.72% ±0.8%14.20% ±0.6%12.36% ±0.6%15.31% ±0.7%
We provide up-to-date daily annualized realized volatilities for individual stocks and ETFs estimated from high-frequency data. We are in the process of incorporating equities from global markets and futures of other asset classes.

We collect trades at their highest frequencies available (up to every millisecond for US equities after 2007), and clean them using the prevalent national best bid and offer (NBBO) that are available up to every second. The mid-quotes are calculated based on the NBBOs, so their highest sampling frequencies are also up to every second.

We provide quasi-maximum likelihood estimates of volatility (QMLE) based on moving-average models MA(q), using non-zero returns of transaction prices (or mid-quotes) sampled up to their highest frequency available, for individual stock-days with at least 12 observations. We select the best model (q) using Akaike Information Criterion (AIC). For comparison, we report realized volatility (RV) estimates using 5-minute and 15-minute subsampled returns.

1. “When Moving-Average Models Meet High-Frequency Data: Uniform Inference on Volatility”, by Rui Da and Dacheng Xiu. 2017.
2. “Quasi-Maximum Likelihood Estimation of Volatility with High Frequency Data”, by Dacheng Xiu. Journal of Econometrics, 159 (2010), 235-250.
3. “How Often to Sample A Continuous-time Process in the Presence of Market Microstructure Noise”, by Yacine Aït-Sahalia, Per Mykland, and Lan Zhang. Review of Financial Studies, 18 (2005), 351–416.
4. “The Distribution of Exchange Rate Volatility”, by Torben Andersen, Tim Bollerslev, Francis X. Diebold, and Paul Labys. Journal of the American Statistical Association, 96 (2001), 42-55.
5. “Econometric Analysis of Realized Volatility and Its Use in Estimating Stochastic Volatility Models”, by Ole E Barndorff‐Nielsen and Neil Shephard. Journal of the Royal Statistical Society: Series B, 64 (2002), 253-280.
B41100 Applied Regression Analysis (MBA)
This course is about regression, a powerful and widely used data analysis technique wherein we seek to understand how different random quantities relate to one another. Students will learn how to use regression to analyze a variety of complex real world problems, with the aim of understanding data and prediction of future events. Focus is placed on understanding of fundamental concepts and development of the skills necessary for robust application of regression techniques. Examples are used throughout to illustrate application of the tools.
B41902 Statistical Inference (PhD)
The focus of this course will be methods to draw inferences in econometric models. We will cover linear regression models, GMM, nonlinear models, and time series models. The majority of the discussion will cover frequentist methods focusing on the use of approximations to finite-sample sampling distributions as a means for obtaining inference. We will cover methods that are appropriate for independent data as well as dependent data. We will discuss intuition for how and when to use the econometric tools developed in the class in addition to deriving some of the relevant theoretical properties.
This website and the information presented on it are for research purposes only and should not be used for investment or other commercial purposes. You may use this website and the information presented on it solely for research purposes and not for any commercial purpose. The University of Chicago (the “University”) does not endorse this website and hereby disclaims all representations and warranties about it and the information presented on it, whether express or implied, including any implied warranties of merchantability, fitness for a particular purpose, and non-infringement. The University specifically disclaims any warranties as to the accuracy, usefulness, truthfulness, and availability of the information presented on this website. By using this website, you agree that you will not make any claim against the University or any of its trustees, officers, employees, agents, or other representatives relating to it or the information presented thereon. Neither the University nor any of its trustees, officers, employees, agents, or other representatives will have any liability to you or anyone else in connection with your use of this website or any information you receive through your use of it.
©2017, Dacheng Xiu at the University of Chicago.