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Kim,  Donggyu Assistant Professor 사진
Kim, Donggyu Assistant Professor
Contact Information
  • Office.S292
  • E-mail.donggyukim@kaist.ac.kr
  • Homepage/Lab. Lab.
Research Areas High-Frequency Finance, Statistics, Machine Learning, Quantum State Tomography
Biography

Education

    Ph.D. in Statistics, University of Wisconsin-Madison, USA

Career

    08/2016-08/2017 Postdoctoral Fellow, Department of Operations Research & Financial Engineering, Princeton University
Publications & Research

Publications (patents, etc.)

    Published/Accepted Papers
    1. Cho. J., Kim, D., and Rohe, K. (2018+). Intelligent Initialization and Adaptive Thresholding for Iterative Matrix Completion; Some Statistical and Algorithmic Theory for Adaptive-Impute. Accepted with minor revision in Journal of Computational and Graphical Statistics.

    2. Kim, D., Kong, X., Li, C., and Wang, Y. (2018). Adaptive Thresholding for Large Volatility Matrix Estimation Based on High-Frequency Financial Data. Journal of Econometrics, 203, 69-79.

    3. Kim, D., Liu, Y. and Wang, Y. (2018). Large Volatility Matrix Estimation with Factor-Based Diffusion Model for High-Frequency Financial data. Bernoulli, 24, 3657-3682.

    4. Fan, J. and Kim, D. (2018+). Robust high-dimensional volatility matrix estimation for high-frequency factor model. Accepted in Journal of the American Statistical Association.

    5. Kim, D., and Wang, Y. (2017). Hypothesis Tests of Large Density Matrices of Quantum Systems Based on Pauli Measurements. Physica A, 469, 31-51.

    6. Cho, J., Kim, D., and Rohe, K. (2017). Asymptotic Theory for Estimating the Singular Vectors and Values of a Partially-observed Low Rank Matrix with Noise. Statistica Sinica, 27, 1921-1948.

    7. Cai, T., Kim, D., Yuan, M., Wang, Y. and Zhou, H. (2016). Optimal Large-Scale Quantum State Tomography with Pauli Measurements. The Annals of Statistics, 44, 682-712.

    8. Kim, D. and Wang, Y. (2016). Unified discrete-time and continuous-time models and statistical inferences for merged low-frequency and high-frequency financial data. Journal of Econometrics,194, 220-230.

    9. Kim, D. and Wang, Y. (2016). Sparse PCA Based on High-Dimensional It\^o processes with Measurement Errors. Journal of Multivariate Analysis, 152, 172-189.

    10. Kim, D., Wang, Y. and Zou, J. (2016). Asymptotic Theory for Large Volatility Matrix Estimation Based on High-Frequency Financial Data. Stochastic Processes and Their Applications, 126, 3527?3577.

    11. Kim, D. (2016). Statistical inference for unified GARCH-Ito models with high-frequency financial data. Journal of Time Series Analysis, 37, 513-532.

    12. Zhang, X., Kim, D., and Wang, Y. (2016). Jump Variation Estimation with Noisy High Frequency Financial Data via Wavelets. Econometrics, 4(3), 34.

    13. Kim, D. and Zhang, C. (2014). Adaptive Linear Step-up Multiple Testing Procedure with the Bias-Reduced Estimator. Statistics and Probability Letters, 87, 31-39.


    Submitted Papers
    14. Cai, T, Kim, D., and Wang, Y. (2018). Optimal Estimation of Eigenspace of Large Density Matrices of Quantum Systems Based on Pauli Measurements. Submitted.

    15. Fan, J. and Kim, D. (2018). Structured Volatility Matrix Estimation for Non-synchronized High-frequency Financial Data. Submitted (2nd round).

    16. Kim, D. and Fan, J. (2018). Factor GARCH-Ito Models for High-frequency Data with Application to Large Volatility Matrix Prediction. Submitted (3rd round).

Research Areas

    Contact : Joo, Sunhee ( shjoo2006@business.kaist.ac.kr )

    Faculty & Research

    KCB ISSUE