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김동규 조교수 사진

Faculty Directory김동규 조교수

  • Contact Information Office.S292 Tel. E-mail.donggyukim@business.kaist.ac.kr Lab. https://sites.google.com/site/donggyukim0329/
  • Academic Groups금융
  • MajorHigh-Frequency Finance, Machine Learning, Quantum State Tomography

학력

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

주요경력

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

산업체자문활동

주요논문 (특허등)

    1. Kim, D., Liu, Y. and Wang, Y. (2017). Large Volatility Matrix Estimation with Factor-Based Diffusion Model for High-Frequency Financial data. Accepted in Bernoulli.

    2. Fan, J. and Kim, D. (2017). Robust high-dimensional volatility matrix estimation for high-frequency factor model. Accepted in Journal of the American Statistical Association.

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

    4. 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. To appear in Statistica Sinica (doi: 10.5705/ss.202016.0205).

    5. 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.

    6. 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.

    7. 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. Supplement Document.

    8. 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.

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

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

    11. 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.

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