Sparse modelling in High dimensional Time series
Long range dependence (LRD) phenomenon in time series analysis
change-point analysis, heavy-tail phenomenon
Education
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
Experience
March 2021: 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
Journal Articles
(2023)
LOCAL WHITTLE ESTIMATION OF HIGH-DIMENSIONAL
LONG-RUN VARIANCE AND PRECISION MATRICES.
ANNALS OF STATISTICS.
51,
6
(2023)
TEST OF CHANGE POINT VERSUS LONG-RANGE DEPENDENCE IN FUNCTIONAL TIME SERIES.
JOURNAL OF TIME SERIES ANALYSIS.
1,
1
(2023)
Detection of multiple change-points in high-dimensional
panel data with cross-sectional and temporal dependence.
STATISTICAL PAPERS.
1,
1
(2023)
Detecting Changes in Correlation Networks with Application to Functional Connectivity of fMRI Data.
PSYCHOMETRIKA.
88,
2
(2022)
Volatility changes in cryptocurrencies: evidence from sparse VHAR-MGARCH model.
APPLIED ECONOMICS LETTERS.
1,
1
(2021)
Robust test for structural instability in dynamic factor models.
ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS.
73,
4
(2021)
Sparse vector heterogeneous autoregressive modeling for realized volatility.
JOURNAL OF THE KOREAN STATISTICAL SOCIETY.
50,
2
(2021)
Two sample tests for high-dimensional autocovariances.
COMPUTATIONAL STATISTICS DATA ANALYSIS.
153,
1
(2020)
Block wild bootstrap-based CUSUM tests robust to high persistence and misspecification.
COMPUTATIONAL STATISTICS & DATA ANALYSIS.
150,
106996
(2020)
Asymptotics of bivariate local Whittle estimators with applications to fractal connectivity.
JOURNAL OF STATISTICAL PLANNING AND INFERENCE.
205,
(2020)
Factor-augmented HAR model improves realized volatility forecasting.
APPLIED ECONOMICS LETTERS.
27,
12
(2019)
Detecting structural breaks in realized volatility.
COMPUTATIONAL STATISTICS & DATA ANALYSIS.
134,
(2018)
Periodic dynamic factor models: estimation approaches and applications.
ELECTRONIC JOURNAL OF STATISTICS.
12,
2
(2017)
Sparse seasonal seasonal and periodic vector autoregressive modeling.
COMPUTATIONAL STATISTICS & DATA ANALYSIS.
106,
1
(2015)
A piecewise polynomial trend against long range dependence.
JOURNAL OF THE KOREAN STATISTICAL SOCIETY.
44,
3
(2015)
TESTS FOR VOLATILITY SHIFTS IN GARCH AGAINST LONG-RANGE DEPENDENCE.
JOURNAL OF TIME SERIES ANALYSIS.
36,
2
(2014)
On integral representations of operator fractional Brownian fields.
STATISTICS & PROBABILITY LETTERS.
92,
(2014)
On distinguishing multiple changes in mean and long-range dependence using local Whittle estimation.
ELECTRONIC JOURNAL OF STATISTICS.
8,
