Faculty & Research

Dacheng Xiu

Assistant Professor of Econometrics and Statistics

Phone :
773 834-7191
Address :
5807 South Woodlawn Avenue
Chicago, IL 60637

Dacheng Xiu studies financial econometrics with an emphases on the statistical inference and the economic implication from high-frequency financial data, including transaction-level equity prices, commodities and EX futures, and index option quotes. His most recent interest includes empirical and mathematical pricing of volatility derivatives.

His work has appeared in the Journal of Econometrics, Journal of the American Statistical Association, and he has been invited to publish in the Journal of Business Statistics and Economic Statistics. Xiu has presented his work at various conferences and university seminars. He also serves as a referee for many journals in econometrics, statistics, and finance.

Xiu earned his PhD and MA in applied mathematics from Princeton University, where he studied at the Bendheim Center for Finance. Before that, he obtained a BS in mathematics from the University of Science and Technology of China in Hefei, China. Additionally, Xiu’s professional experience includes work with TYKHE Capital LLC in New York and Citigroup in their capital markets and banking division.

Outside of academia, Xiu enjoys a variety of sports as well as photography.


2013 - 2014 Course Schedule

Number Name Quarter
41100 Applied Regression Analysis 2014 (Winter)

2014 - 2015 Course Schedule

Number Name Quarter
41100 Applied Regression Analysis 2014 (Fall)

Other Interests

Skiing, swimming, diving, basketball, and photography


Research Activities

Financial Econometrics, Statistics, Empirical Asset Pricing, and Quantitative Finance

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

With Yacine Ait-Sahalia and Jianqing Fan, "High-Frequency Covariance Estimates with Noisy and Asynchronous Data," Journal of the American Statistical Association (2010).

For a listing of research publications please visit ’s university library listing page.

REVISION: Econometric Analysis of Multivariate Realised QML: Estimation of the Covariation of Equity Prices under Asynchronous Trading
Date Posted: Apr  21, 2014
Estimating the covariance between assets using high frequency data is challenging due to market microstructure effects and asynchronous trading. In this paper we develop a multivariate realized quasi-likelihood (QML) approach, carrying out inference as if the observations arise from an asynchronously observed vector scaled Brownian model observed with error. Under stochastic volatility the resulting realised QML estimator is positive semi-definite, uses all available data, is consistent and asymptotically mixed normal. The quasi-likelihood is computed using a Kalman filter and optimized using a relatively simple EM algorithm which scales well with the number of assets. We derive the theoretical properties of the estimator and prove that it achieves the efficient rate of convergence. The estimator is also analysed using Monte Carlo methods and applied to equity data with varying levels of liquidity.

New: Increased Correlation Among Asset Classes: Are Volatility or Jumps to Blame, or Both?
Date Posted: Apr  16, 2014
We develop estimators and asymptotic theory to decompose the quadratic covariation between two assets into its continuous and jump components, in a manner that is robust to the presence of market microstructure noise. Using high frequency data on different assets classes, we find that the recent financial crisis led to an increase in both the quadratic variations of the assets and their correlations. However, we find little evidence to suggest a change between the relative contributions of the Brownian and jump components, as both comove. Co-jumps stem from surprising news announcements that occur primarily before the opening of the U.S. market, and are also accompanied by an increase in Brownian-driven correlations.

REVISION: A Tale of Two Option Markets: Pricing Kernels and Volatility Risk
Date Posted: Feb  10, 2014
Using prices of both S&P 500 options and recently introduced VIX options, we study asset pricing implications of volatility risk. While pointing out the joint pricing kernel is not identified nonparametrically, we propose model-free estimates of marginal pricing kernels of the market return and volatility conditional on the VIX. We find that the pricing kernel of market return exhibits a decreasing pattern given either a high or low VIX level, whereas the unconditional estimates present a U-shape. Hence, stochastic volatility is the key state variable responsible for the U-shape puzzle documented in the literature. Finally, our estimates of the volatility pricing kernel feature a U-shape, implying that investors have high marginal utility in both high and low volatility states.

REVISION: Hermite Polynomial Based Expansion of European Option Prices
Date Posted: Dec  20, 2013
We seek a closed-form series approximation of European option prices under a variety of diffusion models. The proposed convergent series are derived using the Hermite polynomial approach. Departing from the usual option pricing routine in the literature, our model assumptions have no requirements for affine dynamics or explicit characteristic functions. Moreover, convergent expansions provide a distinct insight into how and on which order the model parameters affect option prices, in contrast with small-time asymptotic expansions in the literature. With closed-form expansions, we explicitly translate model features into option prices, such as mean-reverting drift and self-exciting or skewed jumps. Numerical examples illustrate the accuracy of this approach and its advantage over alternative expansion methods.

New: Model-Free Leverage Effect Estimators at High Frequency
Date Posted: Nov  26, 2013
We consider a new nonparametric estimator of the leverage effect, which uses the data on stock prices as well as a certain volatility instrument, such as the CBOE volatility index (VIX) or Black-Scholes implied volatility. The theoretical justification for the new estimator exploits the relationship between the volatility instrument and the spot volatility, together with a certain invariance property of the spot correlation. We derive the asymptotic distribution of the estimator and find that it has good numerical properties in finite samples. We compare this instrument-based estimator with the nonparametric price-only leverage estimator. We demonstrate empirically and in simulations that the price-only estimator is substantially less precise than the instrument-based estimator. Finally, we use the new estimator to provide time series of monthly leverage effects of the S&P 500 index from 2003 to 2012 using two different volatility instruments. We also find that the credit risk, ...

New: Resolution of Policy Uncertainty and Sudden Declines in Volatility
Date Posted: Nov  02, 2013
We introduce downward volatility jumps into a general framework of modeling the term structure of variance. With variance swap data alone, we find that downward volatility jumps are associated with a resolution of policy uncertainty, in particular through statements from Federal Open Market Committee meetings and speeches of Federal Reserve chairmen, and that such jumps are priced with positive risk premia, which reflect the premia for the "put protection" offered by the Federal Reserve. On the modeling side, we explore the structural differences and relative goodness-of-fits of factor specifications, and find that a log-volatility model with two Ornstein-Uhlenbeck factors and two-sided jumps is superior in capturing the volatility dynamics.

New: Spot Variance Regressions
Date Posted: Feb  12, 2013
We study a nonlinear vector regression model for discretely sampled high-frequency data with the latent spot variance of an asset as a covariate. We propose a two-stage inference procedure by first nonparametrically recovering the volatility path from asset returns and then conducting inference based on the generalized method of moments (GMM). The GMM estimator is nonstandard in that the second-order asymptotics is dominated by a bias term, rendering the standard inference implausible. We propos

REVISION: Quasi Maximum Likelihood Estimation of GARCH Models with Heavy-Tailed Likelihoods
Date Posted: Aug  06, 2012
The non-Gaussian maximum likelihood estimator is frequently used in GARCH models with the intention of capturing the heavy-tailed returns. However, unless the parametric likelihood family contains the true likelihood, the estimator is inconsistent due to density misspecification. To correct this bias, we identify an unknown scale parameter that is critical to the identification, and propose a two-step quasi maximum likelihood procedure with non-Gaussian likelihood functions. This novel approach

New: High Frequency Covariance Estimates with Noisy and Asynchronous Financial Data
Date Posted: Jun  28, 2010
This paper proposes a consistent and efficient estimator of the high frequency covariance (quadratic covariation) of two arbitrary assets, observed asynchronously with market microstructure noise. This estimator is built upon the marriage of the quasi-maximum likelihood estimator of the quadratic variation and the proposed Generalized Synchronization scheme. It is therefore not influenced by the Epps effect. Moreover, the estimation procedure is free of tuning parameters or bandwidths and readil

REVISION: Quasi-Maximum Likelihood Estimation of Volatility with High Frequency Data
Date Posted: Jun  28, 2010
This paper investigates the properties of the well-known maximum likelihood estimator in the presence of stochastic volatility and market microstructure noise, by extending the classic asymptotic results of quasi-maximum likelihood estimation. When trying to estimate the integrated volatility and the variance of noise, this parametric approach remains consistent, efficient and robust as a quasi-estimator under misspecified assumptions. Moreover, it shares the model-free feature with nonparametri

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