Donggyu Kim

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

Published/Accepted Papers

• 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.
• 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.
• 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.
• 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.
• Kim, D., and Wang, Y. (2017). Hypothesis Tests of Large Density Matrices of Quantum Systems Based on Pauli Measurements. Physica A, 469, 31-51.
• 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.
• 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.
• 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.
• 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.
• 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.
• Kim, D. (2016). Statistical inference for unified GARCH-Ito models with high-frequency financial data. Journal of Time Series Analysis, 37, 513-532.
• Zhang, X., Kim, D., and Wang, Y. (2016). Jump Variation Estimation with Noisy High Frequency Financial Data via Wavelets. Econometrics, 4(3), 34.
• 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

• Cai, T, Kim, D., and Wang, Y. (2018). Optimal Estimation of Eigenspace of Large Density Matrices of Quantum Systems Based on Pauli Measurements. Submitted.
• Fan, J. and Kim, D. (2018). Structured Volatility Matrix Estimation for Non-synchronized High-frequency Financial Data. Submitted (2nd round).
• Kim, D. and Fan, J. (2018). Factor GARCH-Ito Models for High-frequency Data with Application to Large Volatility Matrix Prediction. Submitted (3rd round).