Choi, S. H. and Kim, D. (2025). Low-Rank Structured Nonparametric Prediction of Instantaneous Volatility. Submitted.
Choi, S. H. and Kim, D. (2025). Large Volatility Matrix Prediction using Tensor Factor Structure. Submitted.
Oh, M. and Kim, D. (2024). Property of Inverse Covariance Matrix-based Financial Adjacency Matrix for Detecting Local Groups. Submitted.
Shin, M. and Kim, D. (2024). Nonconvex High-Dimensional Time-Varying Coefficient Estimation for Noisy High-Frequency Observation. Submitted.
Kim, D., Oh, M., and Shin, M. (2021). High-Dimensional Time-Varying Coefficient Estimation. Submitted.
Shin, M., Kim, D., Wang, Y., and Fan, J. (2021). Factor and Idiosyncratic VAR-Ito Volatility Models for Heavy-Tailed High-Frequency Financial Data. Submitted.
Accepted Papers
[34] Shin, M. and Kim, D. (2025+). Robust High-Dimensional Time-Varying Coefficient Estimation. Forthcoming in Econometric Theory.
[33] Choi, S. H. and Kim, D. (2025+). Matrix-based Prediction Approach for Intraday Instantaneous Volatility Vector. Forthcoming in Journal of Business & Economic Statistics. pdf.
[32] Choi, S. H. and Kim, D. (2025+). Large Global Volatility Matrix Analysis Based on Observation Structural Information. Forthcoming in Econometric Theory. pdf.
[31] Oh, M., Kim, D., and Wang, Y. (2025+). Robust realized integrated beta estimator with application to dynamic analysis of integrated beta. Forthcoming in Journal of Econometrics. pdf.
2025
[30] Han, S., Kim, D., and Kim, H. (2025). Adaptive Thresholding for Iterative Matrix Completion with Heterogeneous Missing Probability: H-AdaptiveImpute. Communications in Statistics - Simulation and Computation, 54 (7), 2507-2524.
2024
[29] Kim, D., Oh, M., Song, X., and Wang, Y. (2024). Factor Overnight GARCH-Ito Models. Journal of Financial Econometrics, 22 (5), 1209–1235. pdf.
[28] Kim, D. and Oh, M. (2024). Dynamic Realized Minimum Variance Portfolio models. Journal of Business & Economic Statistics, 42, 123801249. pdf.
[27] Kim, D. (2024). Exponential Realized GARCH-Ito Volatility Models. Econometric Theory, 40, 790–826. pdf.
[26] Oh, M. and Kim, D. (2024). Effect of the U.S.–China Trade War on Stock Markets: A Financial Contagion Perspective. Journal of Financial Econometrics, 22 (4), 954-1005. pdf.
2023
[25] Kim, D., Shin, M., and Wang, Y. (2023). Overnight GARCH-Itô Volatility Models. Journal of Business & Economic Statistics, 41, 1215–1227. pdf.
[24] Shin, M., Kim, D., and Fan, J. (2023). Adaptive Robust Large Volatility Matrix Estimation Based on High-Frequency Financial Data. Journal of Econometrics, 237, 105514. pdf.
[23] Choi, S. H. and Kim, D. (2023). Large Volatility Matrix Analysis Using Global and National Factor Models. Journal of Econometrics, 235, 1917-1993. pdf.
[22] Kim, D. and Shin, M. (2023). Volatility Models for Stylized Facts of High-Frequency Financial Data. Journal of Time Series Analysis, 44, 262-279. pdf.
2022
[21] Kim, D., Song, X., and Wang, Y. (2022). Unified Discrete-Time Factor Stochastic Volatility and Continuous-Time Ito Models for Combining Inference Based on Low-Frequency and High-Frequency. Journal of Multivariate Analysis, 192, 105091. pdf.
[20] Jung, K, Kim, D., and Yu, S. (2022). Next Generation Models for Portfolio Risk Management: An Approach Using Financial Big Data. Journal of Risk and Insurance, 89, 765-787. pdf.
[19] Chun, D. and Kim, D. (2022). State Heterogeneity Analysis of Financial Volatility Using High-Frequency Financial Data. Journal of Time Series Analysis, 43, 105-124.
[18] Kim, D., Oh, M., and Wang, Y. (2022). Conditional Quantile Analysis for Realized GARCH Models. Journal of Time Series Analysis, 43, 640-665.
2021
[17] Song, X., Kim, D., Yuan, H., and Wang, Y., Zhou, Y., and Cui, X. (2021). Volatility Analysis with Realized GARCH-Ito Models. Journal of Econometrics, 222, 393-410. pdf file.
[16] Cai, T, Kim, D., Song, X., and Wang, Y. (2021). Optimal sparse eigenspace and low-rank density matrix estimation for quantum systems. Journal of Statistical Planning and Inference, 213, 50-71.
2019
[15] 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.
[14] Fan, J. and Kim, D. (2019). Structured Volatility Matrix Estimation for Non-synchronized High-frequency Financial Data. Journal of Econometrics, 209, 61-78. pdf file.
[13] 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. pdf file.
2018
[12] 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. pdf file.
[11] 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. pdf.
[10] 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. pdf.
2017
[9] Kim, D., and Wang, Y. (2017). Hypothesis Tests of Large Density Matrices of Quantum Systems Based on Pauli Measurements. Physica A, 469, 31-51.
[8] 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. pdf.
2016
[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.
[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.
[5] 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.
[4] 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.
[3] Kim, D. (2016). Statistical inference for unified GARCH-Ito models with high-frequency financial data. Journal of Time Series Analysis, 37, 513-532.
[2] Zhang, X., Kim, D., and Wang, Y. (2016). Jump Variation Estimation with Noisy High Frequency Financial Data via Wavelets. Econometrics, 4(3), 34.
2014
[1] 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.