Zhenya Liu, PhD
Professor of Finance and Economics
Prof. Zhenya Liu is a professor at the School of Finance, Renmin University of China, and the EM Normandie, France. He held positions at the University of Birmingham, JP Morgan Futures (China), and the International Monetary Fund (IMF). His global experience is reflected in the interdisciplinary nature of his research, which has been published in top economic and finance journals, such as the Journal of Econometrics, Econometric Theory, Journal of Business and Economic Statistics, International Journal of Forecasting, Review of Quantitative Finance and Accounting, China Economic Review, etc.
Professor Liu is a leading expert in financial econometrics and quantitative investment, and he founded the graduate programs in Quantitative Investment at the Renmin University of China. He currently focuses on changepoint econometrics, functional data analysis, statistical factor models, random matrix and portfolio theory, and stochastic optimal stopping rules in finance.
Research
My primary research interests are in quantitative finance, especially in applying statistical and mathematical methods to the field of big data and financial econometrics, asset pricing, investment management, and China stock markets. I have pursued these interests through stochastic analysis and statistical methods. In the near future, I would like to go further with my research in all these areas.
In addition, I see myself expanding my expertise in these areas through a mixture of quantitative methods and empirical/corporate/green finance in the long term.
Selected Publications
Testing Stability in Functional Event Observations with Applications to IPO Performance, Journal of Business & Economic Statistics, September 2022.
Sequential Monitoring of Changes in Dynamic Linear Models, Applied to the U.S. Housing Market, Econometric Theory, April 2022，38(2), 209-272. https://doi.org/10.1017/S0266466621000104
Sequential monitoring for changes from stationarity to mild non-stationarity, Journal of Econometrics, March 2020, vol. 215, pp. 209-238.