Research

Academic Interests

  • Extreme value theory in statistics, finance, insurance and risk management
  • Copula and tail copula methods
  • Financial econometrics

Publications

  • Y. Hou, X. Xia and G. Gao (2026). Combining Structural and Unstructral Data: A Topic-based Finite Mixture Model for Insurance Claim Data. North American Actuarial Journal, to appear.
  • X. Leng, Y. He, Y. Hou and L. Peng (2026). Asymptotics of CoVaR Inference In Two-Quantile-Regression. Journal of Econometrics, 257, 106301.
  • L. Hong, Y. Hou, Q. Zhang and X. Zhang (2026+). Learning to Simulate: Gen- erative Metamodeling via Quantile Regression. Informs Journal on Computing, to appear.
  • Y. Hu and Y. Hou (2028). Two Sample Tests for Bivariate Heteroscedastic Extremes with a Changing Tail Copula. Statistica Sinica, to appear.
  • Q. Zhong, J. Ji, L. Chen, Y. Hou and D. Li (2027). Inference for A Two- Step Joint Model of Extreme Quantile and Expected Shortfall Regression. Statistica Sinica, to appear.*
  • Q. Zhong and Y. Hou (2026). Tail Risk Equivalent Level Transition and Its Application for Estimating Extreme Lp Quantiles. Statistica Sinica, to appear.
  • J. Ji, Y. Hou and D. Li (2026). Estimation of Extreme Conditional Expectile via Tail Risk Transition from Intermediate Quantile. Statistics and Its Interface, 19(1), 123-141.
  • Y. Hou, J. Li and G. Gao (2025). Insurance Loss Modeling with Gradient Tree-Boosted Mixture Models. Insurance: Mathematics and Economics, 121, 45-62.
  • Z. Li, J. Wang and Y. Hou (2024). Online Prediction of Extreme Conditional Quantiles via B-Spline Interpolation. Statistics and Computing, 34, 204.
  • D. Xiao, Z. Zhang, Y. Hou, and J. Wang (2024). Wideband Spectrum Sens- ing Technology Based on Transposed Sampling. Information Countermeasure Technology, 3(6), 60-70. (Chinese)
  • Z. Zhang, Y. Yang, Y. Hou, D. Ou, and L. Xu (2024). Soft Sensor for Melt- ing Flow Rate Prediction based on Data-enhanced Classification Method. Measurement Science and Technology, 35, 125122.
  • Z. Li, Y. Hou and T. Wang (2024). Recent Advances in Mechanisms of Network Generation: Community, Exchangeability, and Scale-Free Properties. WIREs Computational Statistics, 16(2), 1651.
  • Y. Hou, X. Leng, L. Peng and Y. Zhou (2024). Panel Quantilte Regression for Extreme Risk. Journal of Econometrics, 240(1), 105674.
  • X. Zeng, Y. Hu , C. Pan, and Y. Hou (2024). Exploring Systemic Risk Dynamics in the Chinese Stock Market: A Network Analysis with Risk Transmission Index. Risks, 12(3), 56.
  • J.Wang, Y.Hou, X. Li and T. Wang (2023). EVIboost for the Estimation of Extreme Value Index under Heterogeneous Extremes. Journal of Data Science 21(4), 638-657.
  • S. Zheng, K. Fan, Y. Hou, J. Feng and Y. Fu (2023). Clustering by the Probability Distributions from Extreme Value Theory. IEEE Transactions on Artificial Intelligence 4(2), 292-303.
  • Y. Hou, S. Kang, L. Peng and C. Lo (2022). Three-Step Risk Inference In Insurance Ratemaking. Insurance: Mathematics and Economics 105, 1-13.
  • Y. Hou (2022). A Two-Stage Model for High-Risk Prediction in Insurance Ratemaking: Asymptotics and Inference. Insurance: Mathematics and Economics 104, 283-301.
  • W. Xu, Y. Hou and D. Li (2022). Prediction of Extremal Expectile Based on Regression Models with Heteroscedastic Extremes. Journal of Business & Economic Statistics, 40(2), 522-536.
  • J. Zhang, D. Xiao and Y. Hou (2022). Estimation of anomaly contribution in spectrum data base on MES and backtesting. Journal of Terahertz Science and Electronic Information Technology 20(12), 1277-1284.
  • Y. Hou and X. Wang (2021). Extreme and Inference for Tail Gini Functionals with Applications in Tail Risk Measurement. Journal of the American Statistical Association. 116(535), 1428-1443.
  • Y. He, Y. Hou, L.Peng and H. Shen (2020). Inference for Conditional Value-at-Risk of a Predictive Regression. Annals of Statistics 48, 3443-3464.
  • Y. Hou and X. Wang (2019). Nonparametric Inference for Distortion Risk Measures on Tail Regions. Insurance: Mathematics and Economics 89, 92-110.
  • Y. He, Y. Hou, L. Peng and J. Sheng (2019). Statistical Inference for a Relative Risk Measure. Journal of Business and Economic Statistics 37, 301-311.
  • Y. Hou, D. Li, A. Liu, and L. Peng (2019). Jackknife Empirical Likelihood Test for Equality of Degrees of Freedom in T-Copulas. SCIENCE CHINA Mathematics 63, 789-822.
  • X. Wang, Q. Liu, Y. Hou and L.Peng (2018). Nonparametric Inference for Sensitivity of Haezendonck-Goovaerk Risk Measure. Scandinavian Actuarial Journal 8, 661-680.
  • A. Asimit, R. Gerrard, Y. Hou and L. Peng (2016). Tail Dependence Measure for Modeling Financial Extreme Co-movements. Journal of Econometrics 194, 330-348.
  • A. Liu, Y. Hou and L. Peng (2015). Interval Estimation for a Measure of Tail Dependence. Insurance: Mathematics and Economics 64, 294-305.

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