Name: Song Xinyu
Title: Associate Researcher
Research Direction: Statistical modeling and inference of high-frequency data, high-dimensional matrix analysis, statistical simulation of quantum computing
Courses Taught: High-frequency Data and Quantitative Trading
E-mail: song.xinyu@mail.shufe.edu.cn
Telephone: 021-65901238
Research Project
Serial Number | Project Name | Project Number | Project Source | Start and End Time | Project Funds |
1 | Statistical Modeling and Inference of Complex Financial Data | 72103118 | National Natural Science Foundation of China Youth Project | 240,000 | |
2 | Statistical Methods for Complex Data Based on Network Structures and Their Applications in Quantitative Finance and Smart Healthcare | 21PJC056 | Shanghai Pujiang Talent Program | 500,000 | |
3 | Research on Instantaneous Prediction of Shanghai GDP Growth Rate Based on Nowcasting Model | 2023-Z-U05 | Shanghai Municipal People's Government Development Research Center Project | 120,000 |
Education Background
September 2008 - May 2012 University of Wisconsin-Madison Bachelor of Science
September 2012 - December 2017 University of Wisconsin-Madison Doctor of Philosophy (Specialization in Statistics)
Work Experience
From September 2021 to Present Shanghai University of Finance and Economics Associate Researcher
February 2018 - Present Shanghai University of Finance and Economics Assistant Researcher
December 2018 - December 2019 University of Wisconsin-Madison Visiting Scholar
Research Achievements
1. Song Xinyu, Deng Yuanyuan, Zhou Yong, Yuan Huiling (2025) Multivariate GARCH-Ito Model and Its Application in High-Dimensional Volatility Matrix Prediction. Chinese Journal of Management Science, accepted.
2. Gu, C., Huang, M., Song, X., and Wang, X. (2025) Kernel density estimation in metric spaces. Scandinavian Journal of Statistics, 52(2), 1018-1057.
3. Kim, D., Oh, M., Song, X., and Wang, Y. (2024). Factor overnight GARCH-Ito models. Journal of Financial Econometrics, 22, 1209-1235.
4. Kim, D., Song, X., and Wang, Y. (2022). Unified Discrete-time factor stochastic volatility and continuous-time Ito models for combining inference based on low-frequency and high-frequency. Journal of Multivariate Analysis, 192, 105091.
5. Song, X., Kim, D., Yuan, H., Cui, X., Lu, Z., Zhou, Y., ＆ Wang, Y. (2021). Volatility analysis with realized GARCH-Ito models. Journal of Econometrics, 222, 393-410.
6. Cai, T., Kim, D., Song, X., and Wang, Y. (2021). Optimal sparse eigenspace and low-rank density matrix estimation for quantum systems. Journal of Statistical Planning and Inference, 213, 50-71.
7. Wang, Y., ＆ Song, X. (2020). Quantum Science and Quantum Technology. Statistical Science, 35(1), 51-74.
8. Song, X., ＆ Wang, Y. (2020). GARCH quasi-likelihood ratios for SV model and the diffusion limit. Statistics ＆ Probability Letters, 165, 108817.
9. Song, X., ＆ Wang, Y. (2017). Quasi-Monte Carlo simulation of Brownian sheet with application to option pricing. Statistical Theory and Related Fields, 1(1), 82-91.
