Jia Li | Scholars@Duke

Jia Li
Professor of Economics

Professor Li's research focuses on nonparametric estimation and inference of financial risk factors, such as volatility and jumps, based on high frequency financial data. Such data exhibit a microscopic view of asset price behaviors, but also raise new challenges for econometricians. He is currently working on a project for detecting jumps from the perspective of hedging derivative securities, as well as methods for robust estimation and inference of asset price jumps.

Current Appointments & Affiliations

Professor of Economics, Economics, Trinity College of Arts & Sciences 2020

Contact Information

228G Social Sciences, Box 90097, Durham, NC 27708-0097
Box 90097, Durham, NC 27708
jia.li2@duke.edu
https://sites.google.com/view/jiali/home

Background
Education, Training, & Certifications
- Ph.D., Princeton University 2011
Duke Appointment History
- Associate Professor of Economics, Economics, Trinity College of Arts & Sciences 2017 - 2019
- Assistant Professor in the Department of Economics, Economics, Trinity College of Arts & Sciences 2011 - 2017
Research
Selected Grants
- Parametric and nonparametric regressions on spot volatility awarded by National Science Foundation 2013 - 2017
- Estimation and Inference Methods for Continuous-Time Models awarded by National Science Foundation 2012 - 2013
Publications & Artistic Works
Selected Publications
- Academic Articles
  - Li, J., and Z. Liao. “Uniform nonparametric inference for time series.” Journal of Econometrics 219, no. 1 (November 1, 2020): 38–51. https://doi.org/10.1016/j.jeconom.2019.09.011.
    Full Text
  - Li, J., Z. Liao, and M. Gao. “Uniform nonparametric inference for time series using Stata.” Stata Journal 20, no. 3 (September 1, 2020): 706–20. https://doi.org/10.1177/1536867X20953576.
    Full Text
  - Bollerslev, T., J. Li, A. J. Patton, and R. Quaedvlieg. “Realized Semicovariances.” Econometrica 88, no. 4 (July 1, 2020): 1515–51. https://doi.org/10.3982/ECTA17056.
    Full Text Open Access Copy
  - Bollerslev, T., J. Li, and L. S. S. Chaves. “Generalized Jump Regressions for Local Moments.” Journal of Business and Economic Statistics, January 1, 2020. https://doi.org/10.1080/07350015.2020.1753526.
    Full Text
  - Zhang, C., J. Li, and T. Bollerslev. “Occupation density estimation for noisy high-frequency data.” Journal of Econometrics, January 1, 2020. https://doi.org/10.1016/j.jeconom.2020.05.013.
    Full Text
  - Li, J., V. Todorov, and G. Tauchen. “Jump factor models in large cross-sections.” Quantitative Economics 10, no. 2 (May 1, 2019): 419–56. https://doi.org/10.3982/QE1060.
    Full Text
  - Li, J., V. Todorov, G. Tauchen, and H. Lin. “Rank Tests at Jump Events.” Journal of Business and Economic Statistics 37, no. 2 (April 3, 2019): 312–21. https://doi.org/10.1080/07350015.2017.1328362.
    Full Text
  - Li, J., Y. Liu, and D. Xiu. “Efficient estimation of integrated volatility functionals via multiscale Jackknife.” Annals of Statistics 47, no. 1 (February 1, 2019): 156–76. https://doi.org/10.1214/18-AOS1684.
    Full Text
  - Bollerslev, T., J. Li, and Y. Xue. “Volume, volatility, and public news announcements.” Review of Economic Studies 85, no. 4 (October 1, 2018): 2005–41. https://doi.org/10.1093/restud/rdy003.
    Full Text
  - Li, J., and D. Xiu. “Comment on: Limit of Random Measures associated with the increments of a Brownian Semimartingale.” Journal of Financial Econometrics 16, no. 4 (September 1, 2018): 570–82. https://doi.org/10.1093/jjfinec/nbx034.
    Full Text
  - Li, J., V. Todorov, G. Tauchen, and R. Chen. “Mixed-scale jump regressions with bootstrap inference.” Journal of Econometrics 201, no. 2 (December 1, 2017): 417–32. https://doi.org/10.1016/j.jeconom.2017.08.017.
    Full Text
  - Li, J., V. Todorov, and G. Tauchen. “Adaptive estimation of continuous-time regression models using high-frequency data.” Journal of Econometrics 200, no. 1 (September 1, 2017): 36–47. https://doi.org/10.1016/j.jeconom.2017.01.010.
    Full Text
  - Li, J., V. Todorov, and G. Tauchen. “Robust Jump Regressions.” Journal of the American Statistical Association 112, no. 517 (January 2, 2017): 332–41. https://doi.org/10.1080/01621459.2016.1138866.
    Full Text Open Access Copy
  - Li, J., V. Todorov, and G. Tauchen. “Jump Regressions.” Econometrica 85, no. 1 (January 1, 2017): 173–95. https://doi.org/10.3982/ECTA12962.
    Full Text Open Access Copy
  - Li, J., V. Todorov, and G. Tauchen. “ESTIMATING THE VOLATILITY OCCUPATION TIME VIA REGULARIZED LAPLACE INVERSION.” Econometric Theory 32, no. 5 (October 1, 2016): 1253–88. https://doi.org/10.1017/S0266466615000171.
    Full Text
  - Li, J., V. Todorov, and G. Tauchen. “Inference theory for volatility functional dependencies.” Journal of Econometrics 193, no. 1 (July 1, 2016): 17–34. https://doi.org/10.1016/j.jeconom.2016.01.004.
    Full Text
  - Li, J., and D. Xiu. “Generalized Method of Integrated Moments for High-Frequency Data.” Econometrica 84, no. 4 (July 1, 2016): 1613–33. https://doi.org/10.3982/ECTA12306.
    Full Text
  - Li, J. “Robust estimation and inference for jumps in noisy high frequency data: A local-to-continuity theory for the pre-averaging method.” Econometrica 81, no. 4 (July 1, 2013): 1673–93. https://doi.org/10.3982/ECTA10534.
    Full Text
  - Li, J., V. Todorov, and G. Tauchen. “Volatility occupation times.” Annals of Statistics 41, no. 4 (2013): 1865–91. https://doi.org/10.1214/13-AOS1135.
    Full Text
  - Aït-Sahalia, Y., J. Jacod, and J. Li. “Testing for jumps in noisy high frequency data.” Journal of Econometrics 168, no. 2 (June 1, 2012): 207–22. https://doi.org/10.1016/j.jeconom.2011.12.004.
    Full Text
- Scholarly Editions
  - Li, J., and A. J. Patton. “Asymptotic Inference about Predictive Accuracy Using High Frequency Data,” July 6, 2013.
    Open Access Copy
Teaching & Mentoring
Recent Courses

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Education, Training, & Certifications

Duke Appointment History

Selected Grants

Selected Publications

Academic Articles

Scholarly Editions

Recent Courses