Ke Zhu

Ke Zhu

Associate Professor

Publications

Machine Learning in Finance

  1. Zhang, Y., Zhu, K. and Zhu, Z. (2026), ReSGA: A large tail risk model for learning value-at-risk and expected shortfall. In Submission. PDF The website of ReSGA
  2. Zhu, Z. and Zhu, K. (2026), Machine learning vast dynamic conditional covariance matrices: the spirit of “divide and conquer”. To appear in Management Science. PDF Codes
  3. Zhu, Z. and Zhu, K. (2025), AlphaQCM: Alpha discovery in finance with distributional reinforcement learning. In Proceedings of the 42nd International Conference on Machine Learning, volume 267 of Proceedings of Machine Learning Research, pp. 80463-80479. PMLR. PDF Conference Link Codes Circulated by QuantML
  4. Zhu, Z. and Zhu, K. (2025), Enhancement of price trend trading strategies via image-induced importance weights. In Submission. PDF Codes Circulated by QuantML
  5. Zhu, Z., Zhang, N. and Zhu, K. (2024), Big portfolio selection by graph-based conditional moments method. Journal of Empirical Finance 78, 101533. PDF Journal Link Codes
  6. Yang, X., Zhu, Z., Li, D. and Zhu, K. (2024), Asset pricing via the conditional quantile variational autoencoder. Journal of Business & Economic Statistics 42, 681-694. PDF Journal Link Codes

Panel Data Analysis

  1. Pu, Z., Su, B. and Zhu, K. (2026), Testing for conditional heteroskedasticity in large dynamic panel models. In Submission. PDF
  2. Ren, X., Shu, L., Wang, G. and Zhu, K. (2025), Efficient estimation of large time-varying factor models with double sparsity. In Submission. PDF
  3. Su, B. Zhu, F. and Zhu, K. (2025), Logarithmic spatial heteroscedasticity model with exogenous variables: Ohio’s housing prices. To appear in Spatial Economic Analysis. PDF Journal Link
  4. Su, B. and Zhu, K. (2025), Inference for the panel ARMA-GARCH model when both N and T are large. Major Revision for Journal of Business & Economic Statistics. PDF Codes

Time Series Analysis

  1. Yu, C., Zhu, Z. and Zhu, K. (2026), Conditional quantile adjusted conformal prediction for time series. International Conference on Machine Learning. PDF Conference Link
  2. Zhang, Y., Yu, C., Zhu, K. and Ling, S. (2026), Tensor double autoregressive model with application to heavy-tailed tensor time series data. In Submission.
  3. Zhang, N., Feng, Y., Zhu, Z. and Zhu, K. (2026), A new family of generalized Laguerred gamma distributions: With applications in finance. Major Revision for Journal of Business & Economic Statistics.
  4. Song, K., Luo, D., Li, W.K. and Zhu, K. (2026), Testing for the symmetry of innovation in multivariate time series models. Major Revision for Journal of Econometrics. PDF
  5. Yu, C., Zhu, Z. and Zhu, K. (2025), Tensor dynamic conditional correlation model with applications to portfolio selection. In Submission. PDF
  6. Bai, Q., Ling, S. and Zhu, K. (2025), Testing and estimation of change point in ARMA model with heavy-tailed G-GARCH noises. To appear in Journal of Time Series Analysis. PDF Journal Link
  7. Zhang, N. and Zhu, K. (2026), Quantiled conditional variance, skewness, and kurtosis by Cornish-Fisher expansion. Annals of Applied Statistics 20, 131-147. PDF Journal Link Codes
  8. Song, K., Jiang, F. and Zhu, K. (2025), Estimation for conditional moment models based on martingale difference divergence. Journal of Time Series Analysis 46, 727-747. PDF Journal Link Codes
  9. Zhang, B., Liu, M., Yan, T. and Zhu, K. (2025), Automatic tests for serial correlation and ARCH effect of high-dimensional time series. Statistics and its Interface 18, 399-410. PDF Journal Link Codes
  10. Yu, C., Li, D., Jiang, F. and Zhu, K. (2025), Matrix GARCH model: Inference and application. Journal of the American Statistical Association 120, 1747-1762. PDF Journal Link Codes
  11. Zhu, K. (2023), A new generalized exponentially weighted moving average quantile model and its statistical inference. Journal of Econometrics 237, 105510. PDF Journal Link Supplement+Codes
  12. Jiang, F., Li, D., Li, W.K. and Zhu, K. (2023), Testing and modelling for the structural change in covariance matrix time series with multiplicative form. Statistica Sinica 33, 787-818. PDF Journal Link
  13. Luo, D., Zhu, K., Gong, H. and Li, D. (2023), Testing error distribution by kernelized Stein discrepancy in multivariate time series models. Journal of Business & Economic Statistics 41, 111-125. PDF Journal Link Codes
  14. Wang, G., Zhu, K. and Shao, X. (2022), Testing for the martingale difference hypothesis in multivariate time series models. Journal of Business & Economic Statistics 40, 980-994. PDF Journal Link
  15. Ling, S. and Zhu, K. (2022), Self-weighted LSE and residual-based QMLE of ARMA-GARCH models. Journal of Risk and Financial Management 15, 90. PDF Journal Link
  16. Liu, M., Zhu, F. and Zhu, K. (2022), Modeling normalcy-dominant ordinal time series: An application to air quality level. Journal of Time Series Analysis 43, 460-478. PDF Journal Link
  17. Zhou, J., Jiang, F., Zhu, K. and Li, W.K. (2022), Time series models for realized covariance matrices based on the matrix-F distribution. Statistica Sinica 32, 755-786. PDF Journal Link
  18. Liu, M., Zhu, F. and Zhu, K. (2022), Multifrequency-band tests for white noise under heteroskedasticity. Journal of Business & Economic Statistics 40, 799-814. PDF Journal Link
  19. Wang, G., Zhu, K., Li, G. and Li, W.K. (2022), Hybrid quantile estimation for asymmetric power GARCH models. Journal of Econometrics 227, 264-284. PDF Journal Link
  20. Wang, G., Li, W.K. and Zhu, K. (2021), New HSIC-based tests for independence between two stationary multivariate time series. Statistica Sinica 31, 269-300. PDF Journal Link
  21. Jiang, F., Li, D. and Zhu, K. (2021), Adaptive inference for a semiparametric generalized autoregressive conditional heteroskedasticity model. Journal of Econometrics 224, 306-329. PDF Journal Link
  22. Zhu, K. (2020), Hausman tests for the error distribution in conditionally heteroskedastic models. PDF
  23. Jiang, F., Li, D. and Zhu, K. (2020), Non-standard inference for augmented double autoregressive models with null volatility coefficients. Journal of Econometrics 215, 165-183. PDF Journal Link
  24. Wang, Q. and Zhu, K. (2020), On a measure of lack of fit in nonlinear cointegrating regression with endogeneity. Statistica Sinica 30, 371-396. PDF Journal Link
  25. Li, D. and Zhu, K. (2020), Inference for asymmetric exponentially weighted moving average models. Journal of Time Series Analysis 41, 154-162. PDF Journal Link
  26. Zhu, K. (2019), Statistical inference for autoregressive models under heteroscedasticity of unknown form. Annals of Statistics 47, 3185-3215. PDF Journal Link
  27. Li, D., Guo, S. and Zhu, K. (2019), Double AR model without intercept: An alternative to modeling nonstationarity and heteroscedasticity. Econometric Reviews 38, 319-331. PDF Journal Link
  28. Wang, Q., Wu, D. and Zhu, K. (2018), Model checks for nonlinear cointegrating regression. Journal of Econometrics 207, 261-284. PDF Journal Link
  29. Li, D., Zhang, X., Zhu, K. and Ling, S. (2018), The ZD-GARCH model: A new way to study heteroscedasticity. Journal of Econometrics 202, 1-17. PDF Journal Link
  30. Zhu, K., Li, W.K. and Yu, P.L.H. (2017), Buffered autoregressive models with conditional heteroscedasticity: An application to exchange rates. Journal of Business & Economic Statistics 35, 528-542. PDF Journal Link
  31. Zhu, K. (2016), Bootstrapping the portmanteau tests in weak auto-regressive moving average models. Journal of the Royal Statistical Society, Series B 78, 463-485. PDF Journal Link Codes
  32. Zhu, K. and Li, W.K. (2015), A new Pearson-type QMLE for conditionally heteroskedastic models. Journal of Business & Economic Statistics 33, 552-565. PDF Journal Link
  33. Zhu, K. and Ling, S. (2015), LADE-based inference for ARMA models with unspecified and heavy-tailed heteroscedastic noises. Journal of the American Statistical Association 110, 784-794. PDF Journal Link
  34. Chen, M. and Zhu, K. (2015), Sign-based portmanteau test for ARCH-type models with heavy-tailed innovations. Journal of Econometrics 189, 313-320. PDF Journal Link
  35. Zhu, K. and Li, W.K. (2015), A bootstrapped spectral test for adequacy in weak ARMA models. Journal of Econometrics 187, 113-130. PDF Journal Link
  36. Zhu, K. and Ling, S. (2015), Model-based pricing for financial derivatives. Journal of Econometrics 187, 447-457. PDF Journal Link
  37. Zhu, K., Yu, P.L.H. and Li, W.K. (2014), Testing for the buffered autoregressive processes. Statistica Sinica 24, 971-984. PDF Journal Link
  38. Guo, S., Ling, S. and Zhu, K. (2014), Factor double autoregressive models with application to simultaneous causality testing. Journal of Statistical Planning and Inference 148, 82-94. PDF Journal Link
  39. Ling, S., Zhu, K. and Chong, C.Y. (2013), Diagnostic checking for non-stationary ARMA models with an application to financial data. North American Journal of Economics and Finance 26, 624-639. PDF Journal Link
  40. Zhu, K. (2013), A mixed portmanteau test for ARMA-GARCH model by the quasi-maximum exponential likelihood estimation approach. Journal of Time Series Analysis 34, 230-237. PDF Journal Link
  41. Zhu, K. and Ling, S. (2013), Quasi-maximum exponential likelihood estimators for a double AR(p) model. Statistica Sinica 23, 251-270. PDF Journal Link
  42. Zhu, K. and Ling, S. (2012), The Global weighted LAD estimators for finite/infinite variance ARMA(p, q) models. Econometric Theory 28, 1065-1086. PDF Journal Link
  43. Zhu, K. and Ling, S. (2012), Likelihood ratio tests for the structural change of an AR(p) model to a threshold AR(p) model. Journal of Time Series Analysis 33, 223-232. PDF Journal Link
  44. Zhu, K. and Ling, S. (2011), Global self-weighted and local quasi-maximum exponential likelihood estimators for ARMA-GARCH/IGARCH models. Annals of Statistics 39, 2131-2163. PDF Journal Link

Others

  1. Zhang, N., Yu, H. and Zhu, K. (2026), How effective is the regional joint environmental policy in China? Evidence from inverse difference-in-differences. Journal of Agricultural, Biological, and Environmental Statistics 31, 255-272. PDF Journal Link News report from China Environment News (In Chinese)
  2. Cheung, Y.Y.H., Lam, K.F., Zhang, H., Kwan, C.W., Wat, K.P., Zhang, Z., Zhu, K., Cheung, Y.K. and Yin, G. (2023), A randomized controlled experiment for comparing face-to-face and online teaching during COVID-19 pandemic. Frontiers in Education, 8. PDF Journal Link
  3. Gong, H. and Zhu, K. (2022), Info intervention and its causal calculus. In 1st Conference on Causal Learning and Reasoning, California, United States. PDF Conference Link