Lan Zhang

Lan Zhang is a Professor of Finance at the University of Illinois at Chicago (UIC) in the Department of Finance. Her research focuses on big data in finance and high-frequency financial econometrics, with contributions to volatility estimation methodologies like the two-scale realized volatility (TSRV) and multi-scale realized volatility (MSRV), as well as high-frequency principal component analysis (PCA).

Affiliations & Positions:

• Professor of Finance, UIC (2010–present)
• Tenured Associate Professor, UIC (2008–2010)
• Associate Professor, Carnegie Mellon University (2006–2008)
• Assistant Professor, Carnegie Mellon University (2001–2006)
• Reader, University of Oxford (2009–2010), affiliated with Said School of Business and Oxford-Man Institute of Quantitative Finance
• Visiting Professor, University of Oslo (2016–2017)

Research Interests: Lan Zhang’s work bridges statistics and finance, emphasizing:

• Inference for high-dimensional and high-frequency financial data
• Market microstructure noise correction
• Volatility estimation and forecasting
• Applications of advanced statistical techniques to financial econometrics

Grants & Awards:

• Fellow, Society for Financial Econometrics (2016)
• NSF Grants (2002–2005, 2014–2023)
• National Institutes of Health Grant (2003–2006)
• Morgan Stanley Research Fund (2004–2005)

Editorial Roles:

• Co-Editor, Special Issue on "Big Data in Predictive Dynamic Econometric Modeling" (Journal of Econometrics)
• Associate Editor, Statistics and Its Interface, Annals of Applied Statistics, and Econometric Theory

Labs/Teams: Lan Zhang collaborates with interdisciplinary teams focusing on quantitative finance and econometric methodology development, leveraging her expertise in high-frequency data analysis.