1 |
Baek C, Kechagias S, and Pipiras V (2017). Semiparametric, parametric, and possibly sparse models for multivariate long-range dependence. Wavelets and Sparsity XVII, Vol. 10394, International Society for Optics and Photonics 103941S.
|
2 |
Andersen TG, Bollerslev T, Diebold FX, and Labys P (2003). Modeling and forecasting realized volatility. Econometrica, 71, 579-625.
DOI
|
3 |
Baek C and Park M (2021). Sparse vector heterogeneous autoregressive modeling for realized volatility. Journal of the Korean Statistical Society, 50, 495-510.
DOI
|
4 |
Boubacar-Mainassara Y, Esstafa Y, and Saussereau B (2021). Estimating farima models with uncorrelated but non-independent error terms. Statistical Inference for Stochastic Processes, 24, 549-608.
DOI
|
5 |
Corsi F (2009). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics, 7, 174-196.
DOI
|
6 |
Fox AJ (1972). Outliers in time series. Journal of the Royal Statistical Society: Series B (Methodological), 34, 350-363.
DOI
|
7 |
Kleiner B, Martin RD, and Thomson DJ (1979). Robust estimation of power spectra. Journal of the Royal Statistical Society: Series B (Methodological), 41, 313-338.
DOI
|
8 |
Ripley B, Venables B, Bates DM, Hornik K, Gebhardt A, and Firth D (2013). R package 'MASS'
|
9 |
Fox J and Weisberg S (2002). Robust regression: appendix to An R and S-PLUS companion to applied regression.
|
10 |
Tsay RS (1988). Outliers, level shifts, and variance changes in time series. Journal of Forecasting, 7, 1-20.
DOI
|
11 |
Hawkins D (1980). Identification of outliers
|
12 |
Lee J (2017). Detecting outlier with exponential smoothing in time series. Seoul National University.
|
13 |
Lee K and Baek C (2021). Outlier detection for long memory processes. The Korean Data & Information Science Society, 32, 1205-1218.
DOI
|
14 |
Tsay RS, Pena D, and Pankratz AE (2000). Outliers in multivariate time series. Biometrika, 87, 789-804.
DOI
|