Financial Econometrics

Submitted Papers

• Choi, S. H. and Kim, D. (2023). Large Global Volatility Matrix Analysis Based on Structural Information. Submitted. 

• Kim, D. and Oh, M. (2023). Dynamic Realized Minimum Variance Portfolio models. Submitted. 

• Shin, M. and Kim, D. (2023). Robust High-Dimensional Time-Varying Coefficient Estimation. Submitted.

• Kim, D. 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. 

• Oh, M., Kim, D., and Wang, Y. (2021). Dynamic Realized Beta Models Using Robust Realized Integrated Beta Estimators. Submitted. 

Published/Accepted Papers

• Kim, D., Oh, M., Song, X., and Wang, Y. (2023+). Factor Overnight GARCH-Ito Models. To be appeared in Journal of Financial Econometrics.

• Oh, M. and Kim, D. (2023+). Effect of the U.S.–China Trade War on Stock Markets: A Financial Contagion Perspective. To be appeared in Journal of Financial Econometrics.

• Kim, D. (2023+). Exponential Realized GARCH-Ito Volatility Models. To be appeared in Econometric Theory. pdf.

• Kim, D., Shin, M., and Wang, Y. (2023). Overnight GARCH-Itô Volatility Models. Journal of Business & Economic Statistics, 41, 1215–1227. pdf.

• 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.

• Choi, S. H. and Kim, D. (2023). Large Volatility Matrix Analysis Using Global and National Factor Models. Journal of Econometrics, 235, 1917-1993. pdf.

• 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.

• 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.

• Jung, K, Kim, D., and Yu, S. (2022). Next Generation Models for Portfolio Risk Management: An Approach Using Financial Big Data. To be appeared in Journal of Risk and Insurance, 89, 765-787. pdf. 

• Kim, D., Oh, M., and Wang, Y. (2022). Conditional Quantile Analysis for Realized GARCH Models. Journal of Time Series Analysis, 43, 640-665.

• 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. 

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.