n 教育经历
2009—2013 上海财经大学统计与管理学院，博士
2004—2007 北京师范大学数学科学学院，硕士
2000—2004 武汉大学数学与统计学院，学士
n 工作经历
2015—2018 美国宾夕法尼亚州立大学统计系, 博士后
2019— 西安交通大学经济与金融学院, 教授, 博士生导师
研究方向
大数据统计学习、大语言模型、金融计量、数字金融
科研论文
l 金融计量
1. Zhang, F., Tu, Y. (2025). Threshold expectile regression model for time series data, with Tu, Y., Forthcoming in Oxford Bulletin of Economics and Statistics.
2. Zhang, F., Xie, R., Xiao, Z. (2025). Quantile regression kink with an unknown threshold. Forthcoming in Econometric Reviews.
3. Zhang, F., Ma, Y., Hui, Y. (2025). A direct nonparametric estimator for EVaR of dependent financial returns. Forthcoming in Computational Economics.
4. Zhang, F., Xu,Y., Yuan, D. (2024). Detecting financial contagion using a new nonparametric measure of comovements. International Review of Economics and Finance, 89, 284-296.
5. Zhang, F., Xu, Y., Fan, C. (2023). Nonparametric inference of expectile-based value-at-risk for dependent financial returns with application to risk assessment. International Review of Financial Analysis, 90, 102852.
6. Zhang, F., Yang, J., Ye, M. (2020). A nonparametric maximum likelihood estimation for biased-sampling data with zero inflated truncation. Economics Letters, 194, 109399.
7. Zhang, F., Tan, Z. (2015). A new nonparametric quantile estimate for length-biased data with competing risks. Economics Letters, 137, 10-12.
8. 龙振环,张飞鹏,周小英(2017).带多个变点的逐段连续线性分位数回归模型及应用.数量经济技术经济研究, 8, 150-161.
l 大语言模型、文本分析
9. Gao, H., Zhang, F., Jiang, W., Shu, J., Zheng, F., Wei, H. (2024). On the noise robustness of in-context learning for text generation. Advances in Neural Information Processing Systems (NeurIPS), 37, 16569-16600.
10. 张飞鹏,徐一雄,陈曦,周勇(2024). 基于新闻文本情绪的区间值股票回报预测研究. 计量经济学报, 4, 204-230.
l 复杂数据、大数据的统计学习方法
11. Chen, Y., Fan, C., Zhang, F. (2025). Linear-quadratic quantile regression model with a change point due to a threshold covariate. Forthcoming in Computational Statistics.
12. Zhang, F., Chen, X., Liu, P., Fan, C. (2024). Weighted expectile regression neural networks for right censored data. Statistics in Medicine, 43, 5100-5114.
13. Zhang, F., Li, Q. (2023). Segmented correspondence curve regression for quantifying covariate effects on the reproducibility of high-throughput experiments. Biometrics, 79, 2272-2285.
14. Koch, H., Keller, C., Xiang, G., Giardine, B., Zhang, F., Wang, Y., Hardison, R., Li, Q. (2022). CLIMB: High-dimensional association detection in large scale genomic data. Nature Communications, 13, 6874.
15. Singh, R., Zhang, F., Li, Q. (2022). Assessing reproducibility of high-throughput experiments in the case of missing data. Statistics in Medicine, 41, 1884-1899.
16. Zhang, F., Yang, J., Liu, L., Yu, Y. (2022). Generalized linear-quadratic model with a change point due to a covariate threshold. Journal of Statistical Planning and Inference, 216, 194-206.
17. Zhang, F., Huang, X., Fan, C. (2021). Prediction accuracy measures for time-to-event models with left-truncated and right-censored data. Journal of Statistical Computation and Simulation, 91, 2764-2779.
18. Fan, C., Ding, G., Zhang, F. (2020). A kernel nonparametric quantile estimator for right-censored competing risks data. Journal of Applied Statistics, 47, 61-75.
19. Zhou, X., Zhang, F. (2020). Bent line quantile regression via a smoothing technique. Statistical Analysis and Data Mining, 13, 216-228.
20. Zhang, F., Peng, H., Zhou, Y. (2019). Fine-Gray proportional subdistribution hazards model for competing risks data under length-biased sampling. Statistics and Its Interface, 12, 107-122.
21. Lyu, Y., Xue, L., Zhang, F., Koch, H., Saba, L., Kechris, K., Li, Q. (2018). Condition adaptive fused graphical lasso (CFGL): an adaptive procedure for inferring condition-specific gene co-expression network. PLOS Computational Biology, 14: e1006436.
22. Li, Q., Zhang, F. (2018). A regression framework for assessing covariate effects on the reproducibility of high-throughput experiments. Biometrics, 74, 803-813.
23. Zhang, F., Zhao, X., Zhou, Y. (2018). An embedded estimating equation for additive risk model with biased-sampling data. Science China, Mathematics, 61, 1495-1518.
24. Zhang, F., Li, Q. (2017). A continuous threshold expectile model. Computational Statistics and Data Analysis, 116, 49-66.
25. Yang, T., Zhang, F., Yardimci, Y., Song, F., Hardison, R., Noble, W., Yue, F., Li, Q. (2017). HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient. Genome Research, 27, 1939-1949.
26. Zhang, F., Li, Q. (2017). Robust bent line regression. Journal of Statistical Planning and Inference, 185, 41-55.
27. Yan, Y., Zhang, F., Zhou, X. (2017). A note on estimating bent line quantile regression model. Computational Statistics, 32, 611-630, 2017.
28. Fan, C., Zhang, F., Zhou, Y. (2017). Power-transformed linear quantile regression estimation for censored competing risks data. Statistics and Its Interface, 10, 239-254.
29. Zhang, F., Peng, H., Zhou, Y. (2016). Composite partial likelihood estimation for length-biased and right-censored data with competing risks. Journal of Multivariate Analysis, 149, 160-176.
30. Zhang, F., Chen, X., Zhou, Y. (2014). Proportional hazards models with varying coefficients for length-biased data. Lifetime Data Analysis, 20, 132-157.
31. 王小刚,陈姜猛,张飞鹏(2023).含多变点的删失分位数回归模型统计推断及应用.系统科学与数学，43, 1612-1634.
l 经济、金融与管理中应用
32. Zhang, F., Zhang, Y., Deng, Y. (2025). What drives the ‘synchrony’ and ‘asynchrony’ between China’s stock and bond markets? An adaptive Lasso-DCC-MIDAS model. International Review of Economics and Finance, 101, 104206.
33. Zhou, S., Yuan, D., Zhang, F. (2025). Multiscale systemic risk spillovers in Chinese energy market: Evidence from a tail-event driven network analysis. Energy Economics, 142, 108151.
34. Chen, Y., Luo, Q., Zhang, F. (2025). Systemic risk and network effects in RCEP financial markets: Evidence from the TEDNQR model. North American Journal of Economics and Finance, 76, 102317.
35. Zhang, Z., Zhang, F., Ma, C. (2024). Does carbon emission trading scheme affect excess executive compensation? Evidence from a quasi-natural experiment in China. Energy Economics, 139, 107870.
36. Chen, Y., Liu, Y., Zhang, F. (2024). Coskewness and the short-term predictability for Bitcoin return. Technological Forecasting and Social Change, 200, 123196.
37. Chen, Y., Zhang, L., Zhang, F. (2024). Forecasting crude oil volatility and stock volatility: New evidence from the quantile autoregressive model. North American Journal of Economics and Finance, 74, 102235.
38. Zhang, F., Ma, Y., Yuan, D. (2024). The dynamics of tail risk in the Chinese stock market: An empirical study using the LCARE model. Journal of Systems Science and Information, 12, 709-731.
39. Hong, Y., Zhang, R., Zhang, F. (2024). Time-varying causality impact of economic policy uncertainty on stock market returns: Global evidence from developed and emerging countries. International Review of Financial Analysis, 91, 102991.
40. Li, D., Zhang, F., Yuan, D., Cai, Y. (2024). Does COVID-19 impact the dependence between oil and stock markets? Evidence from RCEP countries. International Review of Economics and Finance, 89, 909-939.
41. Zhang, F., Gao, H., Yuan, D. (2024). The asymmetric effect of G7 stock market volatility on predicting oil price volatility: Evidence from quantile autoregression model. Journal of Commodity Markets, 35, 100409.
42. Zhang, F., Zhang, Z. (2023). Forecasting exchange rate markets volatility of G7 countries: Will stock market volatility help? Applied Economics Letters, 30, 991-999.
43. Yuan, D., Li, S., Li, R., Zhang, F. (2022). Economic policy uncertainty,oil and stock markets in BRIC: evidence from quantiles analysis. Energy Economics, 105972.
44. Chen, Y., Qiao, G., Zhang, F. (2022). Oil price volatility forecasting: Threshold effect from stock market volatility. Technological Forecasting and Social Change, 180, 121704.
45. Zhang, F., Hong, Y., Jiang, Y., Yu, J. (2022). Impact of national media reporting concerning COVID-19 on stock markets in China: Empirical evidence from a quantile regression. Applied Economics, 54, 3861-3881.
46. Yuan, D., Zhang, F., Cui, F., Wang, S. (2021). Oil and BRIC Stock markets before and after COVID-19: a local Gaussian correlation approach. Emerging Markets Finance and Trade, 57, 1592-1602.
47. 张飞鹏,徐一雄,陈艳(2025). 极端事件下股票市场非线性尾部风险测度及溢出效应研究.系统工程理论与实践.
48. 张飞鹏,徐一雄,邹胜轩,陈艳(2022).基于LGCNET多层网络的中国A股上市公司系统性风险度量.中国管理科学, 30, 13-25.
n 科研项目
1. 国家自然科学基金项目: 半参数动态EVaR风险计量模型及其在风险管理中应用, 2022-2025.主持.
2. 国家自然科学基金项目: 金融时序数据的动态分位数回归建模及其应用, 2018-2021.主持,已结题.
3. 国家自然科学青年基金项目: 长度偏差抽样下竞争风险数据的半参数模型推断及其应用, 2015-2017.主持,已结题.
n 学术兼职
中国优选法统筹法与经济数学研究会 理事
中国管理科学与工程学会 理事
全国工业统计学教学研究会 理事
中国优选法统筹法与经济数学研究会数据科学分会 副理事长
中国管理科学与工程学会金融计量与风险管理分会 常务理事
中国现场统计研究会资源与环境统计分会 理事
