Changryong Baek
Professor, Department of Statistics, Sungkyunkwan University
Professor, Department of Statistics, Sungkyunkwan University
Ph.D. in Statistics (2010), University of North Carolina at Chapel Hill
Advisor: Vladas Pipiras
M.S. in Statistics (2005), Seoul National University, Seoul, Korea
Advisor: Byeong-Uk Park
B.S. in Statistics (2003), Seoul National University, Seoul, Korea
March 2021- : Full Professor, Department of Statistics, Sungkyunkwan University
March 2015 - Feb 2021: Associate Professor, Department of Statistics, Sungkyunkwan University
March 2013 - Feb 2015: Assistant Professor, Department of Statistics, Sungkyunkwan University
Sep 2010 - May 2013: Assistant Professor, Department of Mathematics, Ohio University
2017- : Associate Editor, Journal of the Korean Statistical Society
2024- : Associate Editor, Communications for Statistical Applications and Methods
I am interested in the following areas:
Sparse modeling in High dimensional time series
Long range dependence (LRD) phenomenon in time series analysis
Change point detection
Heavy tail phenomenon; extreme value theory
and more..
PHD Students
김영근 (Youngeun Kim, 2025)
Dissertation Title: Bayesian Modeling and Forecasting of High Dimensional Long Range Dependent Time Series
Kim, Y and Baek, C. (2024) Bayesian vector heterogeneous autoregressive modeling, with Young Geun Kim, Journal of Statistical Computation and Simulation, 94:6, 1139-1157.
박민수 (Minsu Park, 2023)
Dissertation Titie: Statistical models for high-dimensional realized volatilities
Park, M. and Baek, C. (2022). Autoencoder factor augmented heterogeneous autoregressive model. The Korean Journal of Applied Statistics, 35(1), 49–62.
Shin, A. J., Park, M. and Baek, C. (2022). Sparse vector heterogeneous autoregressive model with nonconvex penalties, Communications for Statistical Applications and Methods.
Baek, C. and Park, M. (2021). Sparse vector heterogeneous autoregressive modeling for realized volatility. Journal of Korean Statatistical Society. 50, 495–510.
Master Students
박이현(Eehyun Park, 2025) Matrix-valued heterogeneous autoregressive modeling, Journal of the Korean Data & Information Science Society, Vol 35(6), 961-973, 2024.
서용원(Youngwon Seo, 2025) Comparison of time series CV methods for deep learning, Journal of the Korean Data & Information Science Society, Vol 35(3), 397–410, 2024.
엄정민(Jungmin Um, 2025) Detection of structural changes in the covariance matrix using block wild bootstrap, Journal of the Korean Data & Information Science Society, Vol 36(1), 87-99, 2025.
김민지(Minji Kim, 2024) Change-point detection for high-dimensional time series using geometric mapping, Journal of the Korean Data & Information Science Society, Vol 35(1), 47-61, 2024.
김상태(Sangtae Kim, 2024) Banded vector heterogeneous autoregression models, The Korean Journal of Applied Statistics, Vol 36(6), 529-545, 2023.
박정현(Junhyun Park, 2024) Block wild bootstrap for self-normalization based change-point detection, Journal of the Korean Data & Information Science Society, Vol 34(5), 823-835, 2023.
이승아(SeungAh Yi, 2024) Deep learning approaches for interval prediction, Journal of the Korean Data & Information Science Society, Vol 34(6), 893-904, 2023.
문세인 (Sein Moon, 2023) Threshold heterogeneous autoregressive modeling for realized volatility, with Sein Moon and Minsu Park, The Korean Journal of Applied Statistics, Vol 36(4), 295-307, 2023
김경희 (Kyunghee Kim, 2023) Outlier detection for multivariate long memory processes, with Kyunghee Kim, Seungyeon Yu, The Korean Journal of Applied Statistics, Vol 35(3), 395-406
김동영 (Dongyeong Kim, 2023) Robust estimation of sparse vector autoregressive models, The Korean Journal of Applied Statistics, Vol 35(5), 631-644
유승연 (Seungyeon Yu, 2023) Outlier detection for multivariate long memory processes, with Kyunghee Kim, Seungyeon Yu, The Korean Journal of Applied Statistics, Vol 35(3), 395-406
이재원 (Jaewon Lee, 2023) Controlling the false discovery rate in sparse VHAR models using knockoffs, with Minsu Park, The Korean Journal of Applied Statistics, Vol 35(6), 685-701
김문정 (Moonjung Kim, 2022) High-dimensional change point detection using MOSUM-based sparse projection, The Korean Journal of Applied Statistics 35 (1), 63-75
신재호 (Andrew Jaeho Shin, 2022) Sparse vector heterogeneous autoregressive model with nonconvex penalties. Communications for Statistical Applications and Methods.
이승원 (Seungwon Lee, 2022) Volatility changes in cryptocurrencies: evidence from sparse VHAR-MGARCH model, Applied Economics Letters, 2022, DOI: 10.1080/13504851.2022.2064417
이경민 (Kyeongmin Lee, 2022) Outlier detection for long memory processes, Journal of the Korean Data & Information Science Society 2021;32:1205-18
조예나 (Yena Cho, 2022) Matrix Profile as an Exploratory Financial Data Analysis Tool, Journal of the Korean Data Analysis Society (February 2022), 24(1), 67-81.
안광준 (Kwangjoon An, 2021) Value at Risk calculation using sparse vine copula models. The Korean Journal of Applied Statistics, 34(6), 875–887. https://doi.org/10.5351/KJAS.2021.34.6.875
김지영 (Jiyoung Kim, 2019) Bivariate long range dependent time series forecasting using deep learning, The Korean Journal of Applied Statistics, 32(1), 69-81, 2019.
이세훈 (Sehun Lee, 2018) Time series representation for clustering using unbalanced Haar wavelet transformation, The Koran Journal of Applied Statistics, 31(6), 707–719, 2018
김동우 (Dongwoo Kim, 2018) Factor-augmented HAR model improves realized volatility forecasting, Applied Economics Letters, DOI:10.1080/13504851.2019.1657554, 2019
박소진 (Sojin Park, 2017) Time-varying modeling of the composite LN-GPD, The Korean Journal of Applied Statistics, 31(1), 109-122, 2018.
김재율 (Jai-Yool Kim, 2017) Neural network heterogeneous autoregressive models for realized volatility, Communications for Statistical Applications and Methods, 25(6), 659-671, 2018.
김보배 (Bobae Kim, 2016) Threshold estimation for the composite LN-GPD models, with Jisuk Noh, The Korean Journal of Applied Statistics, 29(5), 807-822, 2016.
권용 (Yong Kwon, 2016) Bootstrap estimation of long-run variance under strong dependence, The Korean Journal of Applied Statistics, 29 (3), 449-462, 2016.
이슬기 (Sl Gi Lee, 2016) Adaptive lasso in sparse vector autoregressive models, The Korean Journal of Applied Statistics, 29 (1), 27-39, 2016.
노지숙 (Jisuk Noh, 2015) Threshold estimation for the composite LN-GPD models, with Bobae Kim, The Korean Journal of Applied Statistics, 29(5), 807-822, 2016.
박세린 (SeRin Park, 2015) On multivariate GARCH model selection based on risk management, Journal of the Korean Data & Information Science Society, 25(6), 1333-1343, 2014.
김동인 (Dongin Kim, 2015) Forecasting for periodic autoregressive moving average model
이승규 (Seungkyu Lee, 2015) Filtered coupling measures for variable selection in sparse vector autoregressive modeling, The Korean Journal of Applied Statistics, 28(5), 871-883, 2015.
조여영 (Yeoyoung Cho, 2014) Estimation of long memory parameter in nonparametric regression, Communications for Statistical Applications and Methods, Vol. 26, No. 6, 611–622, 2019
이원석 (Wonseok Lee, 2014) The sparse vector autoregressive model for PM10 in Korea, Journal of the Korean Data & Information Science Society, 25(4), 807-817, 2014.