Dacheng Xiu's Homepage

Biography

DACHENG XIU

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

5807 South Woodlawn Avenue, Chicago, IL 60637
773.834.7191
dacheng.xiu@chicagobooth.edu
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, Journal of Political Economy, Journal of Finance, Review of Financial Studies, Journal of the American Statistical Association, and Annals of Statistics. He is a Co-Editor for the Journal of Financial Econometrics, an Associate Editor for the Review of Financial Studies, Management Science, Journal of Econometrics, Journal of Business & Economic Statistics, Journal of Applied Econometrics, the Econometrics Journal, and Journal of Empirical Finance. He has received several recognitions for his research, including Fellow of the Society for Financial Econometrics, Fellow of the Journal of Econometrics, Swiss Finance Institute Outstanding Paper Award, AQR Insight Award, and Best Conference Paper Prize from the European Finance Association. In 2017, Xiu launched a website that provides up-to-date realized volatilities of individual stocks, as well as equity, currency, and commodity futures. These daily volatilities are calculated from 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 student 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.

Research

PUBLICATIONS

"(Re-)Imag(in)ing Price Trends", with Jingwen Jiang and Bryan Kelly, forthcoming in the Journal of Finance.

Chicago Booth Review CBR Video IPython Imaging Example.html Image Data.zip Machine Learning Cross-Section of Expected Returns Convolutional Neural Networks Transfer Learning Big Data Return Predictability

"Factor Models, Machine Learning, and Asset Pricing", with Stefano Giglio and Bryan Kelly, Annual Review of Financial Economics， Vol. 14, (2022), 1-32.

Survey Factor Model Cross-Section of Expected Returns Machine Learning Big Data Return Predictability

"When Moving-Average Models Meet High-Frequency Data: Uniform Inference on Volatility", with Rui Da, Econometrica, Vol. 89, No. 6, (2021), 2787-2825.

Chicago Booth Review Supplemental Material QMLE Volatility Estimation Microstructure Noise Kalman Filtering Model Selection

"Asset Pricing with Omitted Factors", with Stefano Giglio, Journal of Political Economy, Vol. 129, No. 7, (2021), 1947-1990 (lead article). Winner of the Best Conference Paper Prize at the 44th EFA.

Chicago Booth Review Supplemental Material Slides Matlab Codes.zip Fama-MacBeth Estimator Factor Model PCA Cross-Section of Expected Returns Big Data

"Thousands of Alpha Tests", with Stefano Giglio and Yuan Liao, Review of Financial Studies, Vol. 34, Issue 7, (2021), 3456-3496.

Supplemental Material Python Codes.zip Data Snooping Multiple Testing Hedge Funds Factor Model PCA Matrix Completion Cross-Section of Expected Returns Machine Learning Big Data Missing Data

"Autoencoder Asset Pricing Models", with Shihao Gu and Bryan Kelly, Journal of Econometrics 222 (2021), 429-450.

Machine Learning Time-Series and Cross-Section of Expected Returns Nonlinear Factor Model Neural Networks Big Data Return Predictability Deep Learning

"Empirical Asset Pricing via Machine Learning", with Shihao Gu and Bryan Kelly, Review of Financial Studies, Vol. 33, Issue 5, (2020), 2223-2273. Winner of the 2018 Swiss Finance Institute Outstanding Paper Award.

Chicago Booth Review GitHub Empirical Data (UPDATED June 2021) SAS/Python Codes for Data Supplemental Material Machine Learning Time-Series and Cross-Section of Expected Returns Neural Networks Big Data Return Predictability Deep Learning

"Taming the Factor Zoo: A Test of New Factors", with Guanhao Feng and Stefano Giglio, Journal of Finance, Vol. 75, Issue 3, (2020), 1327-1370. First Prize Winner of the 2018 AQR Insight Award.

Chicago Booth Review Supplemental Material SAS/Python Codes for Data Slides Machine Learning Variable Selection Cross-Section of Expected Returns Big Data

"High-Frequency Factor Models and Regressions", with Yacine Aït-Sahalia and Ilze Kalnina, Journal of Econometrics 216 (2020), 86-105.

High Frequency Fama-French Factors Factor Model

"A Hausman Test for the Presence of Market Microstructure Noise in High Frequency Data", with Yacine Aït-Sahalia, Journal of Econometrics 211 (2019), 176-205.

Matlab Codes.zip Microstructure Noise QMLE Volatility Estimation

"Knowing Factors or Factor Loadings, or Neither? Evaluating Estimators of Large Covariance Matrices with Noisy and Asynchronous Data", with Chaoxing Dai and Kun Lu, Journal of Econometrics 208 (2019), 43-79.

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, Annals of Statistics Vol. 47, No. 1, (2019), 156-176.

Spot (Co)Variance Jackknife Bootstrap

"Principal Component Analysis of High Frequency Data", with Yacine Aït-Sahalia, Journal of the American Statistical Association 114 (2019), 287-303.

Matlab Codes.zip Biplots.mp4 PCA Spot (Co)Variance

"Resolution of Policy Uncertainty and Sudden Declines in Volatility", with Dante Amengual, Journal of Econometrics 203 (2018), 297-315.

Chicago Booth Review Supplemental Material Variance Swaps Pricing Non-affine Models Downward Volatility Jumps VIX

"Econometric Analysis of Multivariate Realised QML: Efficient Positive Semi-definite Estimators of the Covariation of Equity Prices", with Neil Shephard, Journal of Econometrics 201 (2017), 19-42.

Matlab Codes.zip QMLE Factor Model Covariance Estimation Microstructure Noise Kalman Filtering, Smoothing, and EM Algorithm Missing Data

"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

"Generalized Methods of Integrated Moments for High-Frequency Data", with Jia Li, Econometrica 84 (2016), 1613-1633.

Chicago Booth Review Supplemental Material An earlier version: Spot Variance Regressions.pdf High Frequency GMM Spot (Co)Variance

"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

"Incorporating Global Industrial Classification Standard into Portfolio Allocation: A Simple Factor-Based Large Covariance Matrix Estimator with High Frequency Data", with Jianqing Fan and Alex Furger, Journal of Business & Economic Statistics 34 (2016), 489-503, (Special Issue on Big Data).

Chicago Booth Review Matlab Codes.zip High Frequency Fama-French Factors Portfolio Allocation Big Data Covariance Estimation

"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

"Quasi-Maximum Likelihood Estimation of Volatility with High Frequency Data", Journal of Econometrics 159 (2010), 235-250.

QMLE Volatility Estimation Microstructure Noise

WORKING PAPERS

"Predicting Returns with Text Data", with Tracy Ke and Bryan Kelly, Mar. 2022. Winner of the 2019 CICF Best Paper Award.

Chicago Booth Review Text Mining Machine Learning Cross-Section of Expected Returns Big Data Return Predictability Sentiment Analysis Topic Model Correlation Screening

"Business News and Business Cycles", with Leland Bybee, Bryan Kelly, and Asaf Manela, Aug. 2021. Revision Requested.

The Structure of Economic News Website Text Mining Machine Learning Big Data Topic Model

"Inference on Risk Premia in Continuous-Time Asset Pricing Models", with Yacine Aït-Sahalia and Jean Jacod, Feb. 2022.

SoFiE Seminar Video Fama-MacBeth Estimator Factor Model Cross-Section of Expected Returns

"Test Assets and Weak Factors", with Stefano Giglio and Dake Zhang, Jun. 2022. Revision Requested.

BFI FINDING Slides Matlab Codes.zip Fama-MacBeth Estimator Factor Model Supervised PCA Cross-Section of Expected Returns Machine Learning Big Data Correlation Screening Weak Signals

"Disentangling Autocorrelated Intraday Returns", with Rui Da, Jun. 2021. Revision Requested.

QMLE Autocovariance Estimation Microstructure Noise Kalman Filtering Model Selection

"The Statistical Limit of Arbitrage", with Rui Da and Stefan Nagel, Jun. 2022.

Machine Learning Factor Model Cross-Section of Expected Returns MSCI Barra Model Multiple Testing Variable Selection Portfolio Allocation Weak Signals

COMMENTS & BOOK CHAPTERS

"Comment on: Limit of Random Measures Associated with the Increments of a Brownian Semimartingale", with Jia Li, Journal of Financial Econometrics 16(4) (2018), 570-582.

"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.

ETFs	12/02/2022	12/01/2022	11/30/2022	11/29/2022	11/28/2022
0Market S&P500SPY	13.49% ±0.3%	14.86% ±0.4%	18.77% ±0.6%	12.00% ±0.4%	10.94% ±0.3%
MaterialXLB	15.33% ±1.3%	16.93% ±1.6%	21.20% ±2.0%	14.05% ±1.4%	14.36% ±1.1%
IndustrialXLI	14.65% ±0.9%	15.13% ±1.3%	17.28% ±1.2%	11.58% ±1.4%	11.25% ±0.8%
Consumer DiscretionaryXLY	19.63% ±1.1%	19.94% ±1.4%	23.21% ±1.7%	17.60% ±1.0%	17.16% ±0.7%
Consumer StaplesXLP	11.74% ±0.7%	13.94% ±0.6%	16.05% ±0.8%	10.74% ±0.8%	10.84% ±0.6%
Health CareXLV	13.04% ±0.7%	14.02% ±0.8%	15.38% ±0.9%	10.12% ±0.6%	10.45% ±0.5%
FinancialXLF	13.84% ±0.6%	15.97% ±1.2%	18.63% ±1.5%	11.78% ±0.6%	11.33% ±0.6%
TechnologyXLK	20.38% ±1.1%	21.10% ±1.6%	25.82% ±2.1%	16.80% ±1.7%	15.64% ±0.9%
UtilitiesXLU	15.99% ±0.9%	17.27% ±0.9%	19.37% ±1.0%	15.42% ±0.8%	15.95% ±1.2%
EnergyXLE	21.75% ±1.4%	20.38% ±0.7%	24.58% ±1.0%	21.37% ±0.8%	25.03% ±1.1%

Objective
We provide up-to-date daily annualized realized volatilities for individual stocks, ETFs, and future contracts, which are estimated from high-frequency data. We are in the process of incorporating equities from global markets.

Data
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.

Methodology
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 if available) sampled up to their highest frequency available, for 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.

References
1. “When Moving-Average Models Meet High-Frequency Data: Uniform Inference on Volatility”, by Rui Da and Dacheng Xiu. Econometrica, Vol. 89, No. 6, (2021), 2787-2825.
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.

Teaching

B41813 Decoding FinTech (EMBA/MBA)

This course provides a high-level introduction to two rapidly developing technologies: artificial intelligence and blockchain. Artificial intelligence, in particular machine learning and natural language processing algorithms, has been adopted by a variety of real-world FinTech companies that build their business based on credit scoring, fraud detection, real-estate valuation, portfolio management, and quantitative trading. Blockchain technology is the cornerstone of cryptocurrencies, smart contracts, and decentralized finance, a rising industry with great potential to disrupt the future of finance. This course is designed for those who are interested in the inner workings of the technologies, as well as their applications in the FinTech industry. Specifically, it places emphases on the philosophy and intuition behind these technologies, as well as their promises and perils, but not on the technical details. Students are expected to have completed MBA core courses before taking this course. The coding component is optional. Prior coding experience is not required.

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.

B20800 Big Data (Undergraduate)

Big Data is a course about data mining: the analysis, exploration, and simplification of large high-dimensional datasets. Students will learn how to model and interpret complicated `Big Data' and become adept at building powerful models for prediction and classification. Techniques covered include an advanced overview of linear and logistic regression, model choice and false discovery rates, multinomial and binary regression, classification, decision trees, factor models, clustering, the bootstrap and cross-validation. We learn both basic underlying concepts and practical computational skills, including techniques for analysis of distributed data. Heavy emphasis is placed on analysis of actual datasets, and on development of application specific methodology. Among other examples, we will consider investment, consumer database mining, internet and social media tracking, network analysis, and text mining.

2018 SoFiE Summer School

Machine Learning and Finance: The New Empirical Asset Pricing. Program is here.

2021 SoFiE Summer School

Machine Learning in Finance. Program is here.