1 |
Andersen TG, Bollerslev T, Diebold FX, and Labys P (2003). Modeling and forecasting realized volatility. Econometrica, 71, 579-625.
DOI
|
2 |
Baek CR, Davis RA, and Pipiras V (2018). Periodic dynamic factor models: estimation approaches and applications. Electronic Journal of Statistics, 12, 4377-4411.
DOI
|
3 |
Zhang CH (2010). Nearly unbiased variable selection under minimax concave penalty. The Annals of Statistics, 38, 894-942.
DOI
|
4 |
Zhu X (2020). Nonconcave penalized estimation in sparse vector autoregression model. Electronic Journal of Statistics, 14, 1413-1448.
|
5 |
Zou H (2006). The adaptive lasso and its oracle properties. Journal of the American Statistical Association, 101, 1418-1429.
DOI
|
6 |
Baek CR and Park MS (2020). Sparse vector heterogeneous autoregressive modeling for realized volatility. Journal of the Korean Statistical Society, 50, 1-16.
|
7 |
Breheny P and Huang J (2011). Coordinate descent algorithms for nonconvex penalized regression with applications to biological feature selection. The Annals of Applied Statistics, 5, 232.
DOI
|
8 |
Chen J and Chen Z (2008). Extended Bayesian information criteria for model selection with large model spaces. Biometrika, 95, 759-771.
DOI
|
9 |
Corsi F (2009). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics, 7, 174-196.
DOI
|
10 |
Davis RA, Zang PF, and Zheng T (2016). Sparse vector autoregressive modeling. Journal of Computational and Graphical Statistics, 25, 1077-1096.
DOI
|
11 |
Fan Jianqing and Li Runze (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American statistical Association, 96, 1348-1360.
DOI
|
12 |
Kim DW and Baek CR (2020). Factor-augmented HAR model improves realized volatility forecasting. Applied Economics Letters, 27, 1002-1009.
DOI
|
13 |
Lutkepohl H (2005). New Introduction to Multiple Time Series Analysis, Springer Science & Business Media, Springer, New York.
|