KAIST 경영대학 경영공학부 교수 > 교수 & 연구 >KAIST COLLEGE OF BUSINESS
02030101 TMBA TMBA #tm_1th_2 > li:nth-child(3) > ul > li.toy_0 > a 02030101 TMBA TMBA #mprovide > div > div > div.box.box1 > ul > li:nth-child(1) > a 02030201 IMBA IMBA #tm_1th_2 > li:nth-child(3) > ul > li.toy_1 > a 02030201 IMBA IMBA #mprovide > div > div > div.box.box1 > ul > li:nth-child(2) > a 02030301 EMBA EMBA #tm_1th_2 > li:nth-child(3) > ul > li.toy_2 > a 02030301 EMBA EMBA #mprovide > div > div > div.box.box1 > ul > li:nth-child(4) > a 02030401 PMBA PMBA #tm_1th_2 > li:nth-child(3) > ul > li.last.toy_3 > a 02030401 PMBA PMBA #mprovide > div > div > div.box.box1 > ul > li:nth-child(3) > a 02040101 FMBA FMBA #tm_1th_2 > li:nth-child(4) > ul > li.toy_0 > a 02040101 FMBA FMBA #mprovide > div > div > div.box.box3 > ul > li:nth-child(1) > a 02040201 MFE MFE #tm_1th_2 > li:nth-child(4) > ul > li.toy_1 > a 02040201 MFE MFE #mprovide > div > div > div.box.box3 > ul > li:nth-child(3) > a 02040401 IMMBA IMMBA #tm_1th_2 > li:nth-child(4) > ul > li.toy_2 > a 02040401 IMMBA IMMBA #mprovide > div > div > div.box.box3 > ul > li:nth-child(2) > a 02040501 IMMS IMMS #tm_1th_2 > li:nth-child(4) > ul > li.toy_3 > a 02040501 IMMS IMMS #mprovide > div > div > div.box.box3 > ul > li:nth-child(4) > a 02040601 SEMBA SEMBA #tm_1th_2 > li:nth-child(4) > ul > li.toy_4 > a 02040601 SEMBA SEMBA #mprovide > div > div > div.box.box3 > ul > li:nth-child(6) > a 02040701 GP GP #tm_1th_2 > li:nth-child(4) > ul > li.last.toy_5 > a 02040701 GP GP #mprovide > div > div > div.box.box3 > ul > li:nth-child(7) > a 02040701 admission admission #txt > div.sub0303.mt_20 > div.btn_wrap > a 02040701 GP GP #mprovide > div > div > div.box.box3 > ul > li:nth-child(7) > a
본문 바로가기 사이트 메뉴 바로가기 주메뉴 바로가기

김동규 조교수 사진
김동규 조교수
Contact Information
  • Office.S292
  • E-mail.donggyukim@kaist.ac.kr
  • Homepage/Lab. Lab.
Research Areas High-Frequency Finance, Risk Management, Statistics, Machine Learning, Quantum State Tomography
Biography

학력

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

주요경력

    03/2020-present Ewon Assistant Professor, KAIST
    08/2016-08/2017 Postdoctoral Fellow, Department of Operations Research & Financial Engineering, Princeton University
Publications & Research

주요논문 (특허등)

    Manuscripts

    Kim, D. and Wang, Y. (2019). Overnight Volatility Processes with Applications of Value at Risk. Submitted.

    Kim, D. and Yu, S. (2019). Incorporating Financial Big Data in Small Portfolio Risk Analysis: Market Risk Management Approach. Submitted.

    Cho, J., Kim, D., Rohe, K., Wang, S. (2019). The Operating Principle of Regularized Spectral Clustering. Submitted.

    Kim, D., Song, X., and Wang, Y. (2019). Unified Discrete-Time Factor Stochastic Volatility and Continuous-Time Ito Models for Combining Inference Based on Low-Frequency and High-Frequency. Submitted.

    Song, X., Kim, D., Yuan, H., and Wang, Y., Zhou, Y., and Cui, X. (2019). Volatility Analysis with Realized GARCH-Ito Models. Submitted.

    Cai, T, Kim, D., Song, X., and Wang, Y. (2019). Optimal Estimation of Eigenspace of Large Density Matrices of Quantum Systems Based on Pauli Measurements. Submitted.


    Published Papers

    2019
    Cho. J., Kim, D., and Rohe, K. (2019). Intelligent Initialization and Adaptive Thresholding for Iterative Matrix Completion; Some Statistical and Algorithmic Theory for Adaptive-Impute. Journal of Computational and Graphical Statistics, 28, 323-333. pdf file.
    Fan, J. and Kim, D. (2019). Structured Volatility Matrix Estimation for Non-synchronized High-frequency Financial Data. Journal of Econometrics, 209, 61-78.
    Kim, D. and Fan, J. (2019). Factor GARCH-Ito Models for High-frequency Data with Application to Large Volatility Matrix Prediction. Journal of Econometrics, 208, 395-417.

    2018
    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. Journal of the American Statistical Association, 113, 1268-1283.

    2017
    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.

    2016
    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. Supplement Document.
    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.

    2014
    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.

연구분야

    만족도조사

    이 페이지에서 제공하는 정보에 대하여 만족하십니까?

    콘텐츠담당자 : 주선희 연락처 : 02-958-3121

    교수 & 연구

    관심자등록

    금융

    KCB ISSUE